ENHANCED DISTRIBUTED MULTIMEDIA SERVICESUSING ADVANCED NETWORK TECHNOLOGIES

By

SUNGWOOK CHUNG

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOLOF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2010

c© 2010 Sungwook Chung

2

Dedicated to my family

3

ACKNOWLEDGMENTS

I would like to thank all people who provided me with their help during my Ph.D

years.

First of all, I would like to thank my advisor, Dr. Jonathan C.L. Liu for all his support

and encouragement. Without his constant inspiration, this dissertation would not have

been possible. The discussion with him on any topic has also made me pleasant and

relaxed.

I am also grateful to my supervisory committee members, Dr. Shigang Chen,

Dr. Douglas D. Dankel II, Dr. Paul Fishwick, and Dr. Paul W. Chun for their invaluable

suggestions and comments for my research.

In addition, I would thank all my Korean friends and families who shared happy

memories in Gainesville.

Last but not least, I truly appreciate my parents, Sangkab Chung and Malnam Seo,

who have been supporting me and have always stood behind me for my whole life,

heartfeltly believing in me without any doubt even at a moment. I also appreciate my

sister, Kyungae Chung, who has pleasantly helped me out with her warm heart all the

time. I would also like to thank all my family members and friends in Korea.

4

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

LIST OF ALGORITHMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

CHAPTER

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.2 Problem Nature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.2.1 Problem Definitions and Requirements . . . . . . . . . . . . . . . . 181.2.2 Proposed Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.2.2.1 Design of Multiple-Loop Architecture . . . . . . . . . . . . 191.2.2.2 An Efficient Storage Scheme . . . . . . . . . . . . . . . . 201.2.2.3 A Practically Constructible Multiple-Loop Architecture . . 21

1.3 Technical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221.3.1 Multimedia-enabled Small Area Network . . . . . . . . . . . . . . . 221.3.2 Fiber Channel Arbitration Loop . . . . . . . . . . . . . . . . . . . . 23

1.4 Outline of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2 A SCALABLE PVR-BASED CONTENT SHARING ARCHITECTURE . . . . . . 26

2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.3 A Scalable TV Content Sharing Architecture . . . . . . . . . . . . . . . . . 30

2.3.1 Single Loop Architecture . . . . . . . . . . . . . . . . . . . . . . . . 302.3.2 Multiple Loop Architecture . . . . . . . . . . . . . . . . . . . . . . . 31

2.4 Design of a Scalable Loop Topology . . . . . . . . . . . . . . . . . . . . . 322.4.1 Linear Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342.4.2 Ring Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362.4.3 Complete Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . 372.4.4 Edge-Added Topology . . . . . . . . . . . . . . . . . . . . . . . . . 382.4.5 Multiple-Interfaced Shared Disks . . . . . . . . . . . . . . . . . . . 38

2.5 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422.5.1 Scalability Comparison among Different Topologies . . . . . . . . . 422.5.2 Impact of the Number of Interfaces per Disk . . . . . . . . . . . . . 45

2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5

3 A STORAGE SAVING SCHEME TO SHARE HD-QUALITY CONTENT . . . . . 48

3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.3 TV Content Distribution Architecture for Community Networks . . . . . . . 503.4 Extending Content Storage Hours . . . . . . . . . . . . . . . . . . . . . . 53

3.4.1 Design Issues for Storage Efficiency . . . . . . . . . . . . . . . . . 543.4.2 Impact of Duplicated Storage of Programs . . . . . . . . . . . . . . 543.4.3 Storage Saving Schemes . . . . . . . . . . . . . . . . . . . . . . . 573.4.4 Replacement Schemes . . . . . . . . . . . . . . . . . . . . . . . . 59

3.5 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.5.1 Effectiveness of Proposed Schemes in a Single Loop . . . . . . . . 623.5.2 Effectiveness of Proposed Schemes in Multiple Loops . . . . . . . 633.5.3 Effectiveness of Our Proposed Architecture . . . . . . . . . . . . . 673.5.4 Impact of PVRs’ Storage Portion for Time-Shifting . . . . . . . . . . 673.5.5 Impact of Storage Capacity . . . . . . . . . . . . . . . . . . . . . . 68

3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4 AN MST-BASED NETWORK ARCHITECTURE FOR SHARING BROADCASTTV PROGRAMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734.3 TV Content-Sharing Architecture . . . . . . . . . . . . . . . . . . . . . . . 73

4.3.1 Multiple Loop Architecture . . . . . . . . . . . . . . . . . . . . . . . 734.4 Enhanced Multiple-loop Network Architecture . . . . . . . . . . . . . . . . 78

4.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.5 Problem Definition and Formulation . . . . . . . . . . . . . . . . . . . . . 80

4.5.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 804.5.2 Minimum Spanning Tree . . . . . . . . . . . . . . . . . . . . . . . . 834.5.3 Minimum Spanning Tree-based Graph . . . . . . . . . . . . . . . . 84

4.6 Performance Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . 874.6.1 Average Loop Size . . . . . . . . . . . . . . . . . . . . . . . . . . . 894.6.2 Total Traffic and Average Reject Ratio . . . . . . . . . . . . . . . . 90

4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5 CONCLUSIONS AND FUTURE WORK . . . . . . . . . . . . . . . . . . . . . . 94

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

BIOGRAPHICAL SKETCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

6

LIST OF TABLES

Table page

2-1 Traffic on each edge in a six loop linear topology . . . . . . . . . . . . . . . . . 35

2-2 Traffic on each edge in a six loop ring topology . . . . . . . . . . . . . . . . . . 36

2-3 m values in the complete topology . . . . . . . . . . . . . . . . . . . . . . . . . 39

2-4 Simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3-1 PVRs’ Contributable Storage in the PVR-based FC-AL system . . . . . . . . . 56

3-2 Symbols for replacement algorithms . . . . . . . . . . . . . . . . . . . . . . . . 58

3-3 Simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3-4 The ratio of pvrs and network disks in a community network . . . . . . . . . . . 66

4-1 Simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

7

LIST OF FIGURES

Figure page

1-1 Overview of TV content broadcasting and sharing system . . . . . . . . . . . . 15

2-1 Overall TV content distribution architecture . . . . . . . . . . . . . . . . . . . . 29

2-2 Examples of multiple loop architectures . . . . . . . . . . . . . . . . . . . . . . 31

2-3 Examples of topology with six loops . . . . . . . . . . . . . . . . . . . . . . . . 33

2-4 Examples of complete topologies using multiple-interfaced shared disks . . . . 40

2-5 Total traffic of different topologies according to the number of loops . . . . . . . 43

2-6 Number of shared disks required by different topologies according to the numberof loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2-7 Number of attached devices of different topologies according to the numberof loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2-8 Number of attached devices in the complete topology according to the numberof interfaces per disk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2-9 Average block overhead time according to the number of interfaces per disk . 47

3-1 Examples of multiple loop architectures . . . . . . . . . . . . . . . . . . . . . . 52

3-2 Probability of storing each program according to its popularity . . . . . . . . . . 55

3-3 Effects of PVRs and network disks . . . . . . . . . . . . . . . . . . . . . . . . . 64

3-4 Effects of Palive and threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

3-5 Effectiveness of our proposed architecture . . . . . . . . . . . . . . . . . . . . . 66

3-6 Impact of PVRs’ storage portion for time-shifting . . . . . . . . . . . . . . . . . 68

3-7 Impact of storage capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4-1 Overall TV content distribution architecture . . . . . . . . . . . . . . . . . . . . 74

4-2 How to relay a TV program using shared disks in a triple-loop . . . . . . . . . . 76

4-3 Examples of CG, MST, and MSG architectures . . . . . . . . . . . . . . . . . . 79

4-4 MST formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4-5 MSG formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4-6 Flowchart for load-balanced MSG . . . . . . . . . . . . . . . . . . . . . . . . . 86

8

4-7 Impact on average setup time in a double-loop . . . . . . . . . . . . . . . . . . 89

4-8 Average loop size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

4-9 Total traffic and average reject ratio . . . . . . . . . . . . . . . . . . . . . . . . . 91

9

LIST OF ALGORITHMS

Algorithm page

3-1 Program placement algorithm (input: new program) . . . . . . . . . . . . . . . 59

3-2 Replacement algorithm for PVRs (input: new program) . . . . . . . . . . . . . 60

3-3 Replacement algorithm for network disks (input: new program) . . . . . . . . . 61

10

Abstract of Dissertation Presented to the Graduate Schoolof the University of Florida in Partial Fulfillment of theRequirements for the Degree of Doctor of Philosophy

ENHANCED DISTRIBUTED MULTIMEDIA SERVICESUSING ADVANCED NETWORK TECHNOLOGIES

By

Sungwook Chung

August 2010

Chair: Jonathan C.L. LiuMajor: Computer Engineering

A variety of enhanced multimedia services, including multimedia live streaming and

high-quality content sharing, have been enabled by the recent advances in network,

storage, and compression technologies. Especially, the improvement of the network

technologies has allowed the organization of a peer-to-peer (P2P) network where

people can easily share their content with others.

Integrated with the progress in multimedia technology, the advent of new electronic

devices has accelerated to achieve those advanced multimedia services efficiently.

In particular, a personal video recorder (PVR), one of those electronic devices, has

emerged as an effective peer device in a P2P network, since it can store broadcast TV

programs on its own embedded hard disk.

In this dissertation, we develop an efficient network architecture such that all users

can access high-quality multimedia content easily and the system can support various

channels of access to the content. In order to achieve these goals, we adopt two leading

technologies in our architecture in addition to a pool of disks as a backup storage; a

fiber channel arbitration loop (FC-AL) as a reliable and broadband network connection

and PVR as a peer in a P2P network. We have thus demonstrated the feasibility of a

PVR-based network architecture by supporting the high-quality content sharing and

distribution using the FC-AL. In fact, the promotion of the storage pool and the PVR

11

capability on the FC-AL loop has enabled the system to become quite feasible for

serving the emerging P2P streaming services.

Nevertheless, the suggested PVR-based FC-AL network architecture has a critical

limitation in terms of the number of attachable PVRs, since the FC-AL is intrinsically

based on a single loop organization and the single loop only allows 127 attachable

devices.

We therefore develop a novel multiple-loop architecture providing a scalable

network organization without using expensive switches. To connect multiple loops in our

architecture, we then introduce shared disks that relay TV programs between loops like

bridges. When extending into a multiple-loop architecture using the shared disks, it is

realized that a topology design for the multiple-loop architecture has a major effect on

the imposed total traffic in the whole system. We thus present and compare all possible

topologies including linear, ring, edge-added, and complete graph (CG) topology. Finally,

the analysis and experimental results reveal that the CG topology among all the possible

topologies provides the best scalability.

In addition, we recognize that, as the network size grows using the multiple-loop

architecture, popular programs tend to be stored redundantly in many PVRs due to

users’ viewing skewness, thereby wasting the storage space that could otherwise be

used to store additional TV programs. Thus, we also propose to extend program storage

hours in terms of the whole system by presenting efficient storage saving algorithms

for both PVRs and a backup storage. Through extensive simulations, we finally show

that our proposed schemes significantly extend program storage hours by an average of

69.7%.

Lastly, we present a practically constructible architecture solution, named MSG,

which maintains both advantages of CG and MST. Thus, the MSG can be expected

to be employed in a real constructible network infrastructure by its balance between

system performance and cost.

12

CHAPTER 1INTRODUCTION

1.1 Overview

The rapid advances in multimedia technologies including network, storage, and

compression technologies have led various advanced multimedia services, such as

video-on-demand (VOD) and HD-quality content distribution in our society. It is also

very common that people can enjoy those multimedia services using various electronic

devices, including personal digital assistants (PDAs), mobile phones, or portable

multimedia players (PMPs). In addition, with the invention of MPEG (Motion Picture

Expert Group) standards such as MPEG-2/4/7/21 [75, 80, 81], people can easily use

and utilize consumer electronic devices to receive and store high-quality TV programs

or video titles. Typical examples of the electronic devices are video cassette recorders

(VCRs), DVD player/recorder and digital video recorders (DVRs) with a hard disk such

as TiVo.

At the same time, the advances in networking technologies have enabled people to

experience various channels for enjoying live streaming services, such as broadcasting

TV programs, and to share their content much more efficiently in a way that a user can

access other users’ content, and vice versa. In particular, the progress of the network

technologies have accelerated to distribute high-quality multimedia contents and to

share their contents as a peer-to-peer (P2P) network.

Fundamentally, the distributed multimedia services try to deliver live multimedia

contents, i.e., streaming video/audio, via networks in real time. Thus, the rapid advances

in computing technology, compression technology, large bandwidth storage devices,

and high speed network technology have made it feasible to provide realtime multimedia

services over the networks. In order to achieve this goal, various issues have to be

considered so that an end user can easily enjoy the live multimedia services. Hence,

the main distributed multimedia research issues are as follows: multimedia content

13

compression, application-layer quality of service (QoS), continuous data distribution

medium, and streaming servers.

• Multimedia Content Compression: The size of multimedia data is quite substantialcompared to regular Web-based data. Thus, in order to support a realtimeplayback at the user side, it is essential to compress the raw multimedia data.The well-known compression methods include MPEG-1/MPEG-2/MPEG-4, H.120,and H.262/H.263/H.264 [24, 58, 66].

• Application-layer Quality of Service (QoS): It is crucial that the quality of realtime-deliveredmultimedia data can satisfy an end user’s expected service quality. In order tomeet the requirements, various application-layer QoS control techniques on the topof possible IP protocols have been presented in [20, 69, 77, 82].

• Continuous Data Distribution Medium: In order to deliver multimedia data inrealtime effectively, it is essential to serve an appropriate network support sincethe network support can reduce transmission delay and data loss ratio. Especially,it is desirable to provide large and reliable network bandwidth for the multimediadata transfer [2, 27, 32].

• Streaming Servers: To offer quality streaming services, streaming servers arerequired to process multimedia data under timing constraints. That is, a next datablock needs to be transmitted completely before the previously delivered datablock is processed by a given playback rate. In addition, the streaming servers cansupport a large capacity of storage in order to hold various multimedia data sincethe size of multimedia data is intrinsically considerable [23, 26, 29, 40, 72].

We consider these fundamental facts to develop an effective TV content sharing

and distribution network architecture. Thus, in order to handle the broadcast TV

programs, our system supports the MPEG-2 compression in addition to the reliable

and broadband network technology by which our network architecture can deliver

HD-quality TV programs. Furthermore, our network architecture maintains minimized

server-functional system components for a target program routing and a backup

storage. That is, in order to support an appropriate QoS control, our system provides the

necessary information to find and retrieve a requested program. Also, a distributed/networked

system storagecan offer the backup storage function in our system. Thus, our system

can provide the mentioned four key functions so that it can serve effectively as a

distributed multimedia system.

14

A Various TV content distributions

......

B P2P network in a single building

Small Area

Networks

C Small area P2P network

Figure 1-1. Overview of TV content broadcasting and sharing system

Specifically, Fig. 1-1A illustrates possible TV content distributions using the

advanced network architecture demonstrated in Fig. 1-1B, where users can easily

obtain various TV programs and they can share their content with others using

home content-storing devices. Particularly, people enjoy the TV programs as they

are broadcast or from the TV-content providing server via a global network, i.e., the

Internet. In addition, small area networks in the community can be formed as P2P

15

networks as shown in Fig. 1-1C. The P2P network provider can serve several ways

for content-providing with the pool of storage devices for large capacities and reliable

backup storages while all users within the P2P networks can do content-sharing

between the peers.

How to build these P2P networks with effective schemes is the major theme of this

study. It is indeed an important topic since there have been several research efforts

[31, 59] that have tried to facilitate all of those state-of-art technologies together, such

as advanced network technologies and electric appliances, for enhanced multimedia

services. However, these systems do not necessarily guarantee the overall system

performance and efficiency since they are based on Internet infrastructure. Via a pilot

study [38] based on the Fiber Channel Arbitration Loop (FC-AL), we have demonstrated

the feasibility of a PVR-based network architecture by supporting high-quality content

sharing and distribution, where PVRs can work as peers via the FC-AL loop. The FC-AL

is one of leading network technologies, offering such advantages as large bandwidth

(e.g., up to 1Gbps), long distance coverage (e.g., 10km), and a fairness arbitration

algorithm, which is a typical structure to organize storage area networks (SANs)

[56, 70, 73, 74].

One unique design consideration that we have built into the system is the promotion

of Digital Video Recorder(DVR) to the concept of Personal Video Recorder(PVR).

Traditionally, VCR users would record the broadcast programs on video home system

(VHS) tapes. Similarly, today’s users can simply store the digital video, including

broadcast movies and TV programs, on the embedded hard disk in the DVRs. Just like

VHS tapes with recorded video content can be shared with friends, the user-stored

digital video on the hard disks can also be shared among friends. Instead of physically

sending the tapes or DVDs, digital video content can be shared simply by on-the-fly

transmission. At this moment, DVRs on the market have only limited transmission

capability (mainly for downloading the program details). It is expected that eventually

16

the DVR will be equipped with high-speed networking interfaces. Therefore, DVRs

have evolved into “Personal Video Recorders” (PVRs) for the next-generation DVRs,

which will be used in the rest of this dissertation as the P2P electronic devices for

content-sharing. Consequently, with the enabled transmission capability, a true P2P

system can be achieved among PVR users with their stored contents.

Due to the promotion of the storage disk pool and the PVR capability on FC-AL

loops, the system becomes quite flexible on supporting the emerging P2P streaming

services. Usually, a multimedia streaming service can be classified in one of the

following: pure streaming, implicit duplication, and explicit duplication. In pure streaming,

a video file is played in real time without storing onto a local hard disk. The implicit

duplication specifies that a certain portion of the video is buffered, in order to support

an effective media playing without enduring a delay or a jitter. Lastly, in the explicit

duplication, the entire video file should be downloaded for playing the file. Our system

can support the implicit and explicit duplication as ways of P2P content-sharing, since

our architecture has the capability that stores a program using the embedded local disk

in PVRs and transmits a received content in realtime with the high-speed network, i.e.,

FC-AL.

In other words, a program can be shared in a way that a PVR is receiving

and transmitting it to the other PVRs at the same time. In fact, with all advanced

functions combined, PVRs have the potentials to emerge as one of the advanced

multimedia electric appliances that support various advanced services, such as

VCR-like operations, electronic program guides (EPGs), and time-shifting TV programs.

In particular, we are interested in an intensive investigation on the degree of time-shift.

Fundamentally, PVRs are able to store realtime broadcast TV programs on their own

embedded hard disks. Thus, the advent of PVRs has led to the change of people’s

TV viewing patterns [52] because people can become independent of the specific TV

broadcasting time with the PVR’s time-shifting functionality. Significantly, the PVRs have

17

enabled the stored TV programs to be shared among the PVR users like a P2P network

as shown in Fig. 1-1.

1.2 Problem Nature

1.2.1 Problem Definitions and Requirements

Fundamentally, it is very essential to support a broadband network bandwidth in

order to serve high-quality multimedia services. In addition, it is also important to satisfy

realtime constraints for such multimedia services.

Moreover, to build up a multimedia-service-enabled network architecture, we should

adopt an appropriate broadband network as a network connection. In addition, each

user can have a multimedia-compatible appliance to enjoy and share the multimedia

contents effectively.

Furthermore, several system components as well as PVRs are also necessary in

order to coordinate each system component and to support various access channels for

multimedia contents, such as backup storages. Since each PVR is under an individual

user’s control, we also need to develop a scheme to utilize the whole system effectively,

regardless of each user’s discretion.

Last but not least, a network architecture has to be scalable without a limitation on

the number of accommodable users, so that the network architecture can be applicable

for various purposes, while still supporting its originated advantages.

1.2.2 Proposed Schemes

In order to provide high-quality multimedia services within a small area like a single

building, the feasibility of an FC-AL-based architecture for sharing high-quality TV

programs between PVRs has been investigated. The suggested architecture consists

of PVR users, a group of network disks, the content management server and FC-AL

connection to share and distribute high-quality programs reliably and efficiently. As a

matter of fact, that architecture has been shown to perform effectively with extensive

simulations [38].

18

1.2.2.1 Design of Multiple-Loop Architecture

Although its feasibility and effectiveness has been thoroughly examined and

explored successfully, it has a critical limitation in terms of scalability, i.e., the number

of accommodable PVR users, despite its advantages such as various multimedia

services at a low cost, since the suggested architecture is merely based on a single-loop

FC-AL structure, Specifically, the FC-AL standard allows only 7-bit address space [34],

which means that all attachable PVRs are limited at most to 127 PVRs. Hence, this

indicates that the single-loop architecture is not suitable where many users have to be

accommodated by the network architecture, such as a densely populated building or a

regional network area like a community or a campus.

In this dissertation, we first develop a scalable PVR-based network architecture

based on the FC-AL-based network with multiple loops. There can be several considerations

to determine a better design. Fundamentally, a topology design affects the configuration

of a multiple-loop architecture and the organized multiple-loop architecture has its own

characteristics according to the selected topology design. For example, if a multiple-loop

architecture is configured by a linear topology, the middle loops and shared disks have

to be involved more than both end loops but have the least connectivity between loops.

On the other hand, a ring topology imposes less traffic load in the middle loops and

shared disks but requires more connectivity compared to the linear topology. Noticeably,

the topology design for a multiple-loop architecture has an effect on the traffic load and

the connectivity, which is closely related to the number of necessary shared disks.

An additional observation is that the obtainable scalability, i.e., the number of

attachable devices including PVRs, network disks, and shared disks, is affected by

the number of required shared disks. That is, if a multiple-loop architecture needs

more shared disks, the multiple-loop architecture provides less scalability at the cost

of deployed shared disks with respect to the given number of loops. In other words, if a

19

network architecture wants to accommodate more PVR users with the given number of

loops, it is more desirable that the architecture topology can support more scalability.

Therefore, we analyze and compare possible multiple-loop topologies: a linear

topology, a ring topology, an edge-added topology, and a complete topology. With the

goal of finding the topology to provide the most scalability, we determine which topology

design is the most suitable for the multiple-loop network architecture, enabling many

more PVRs to share and distribute high-quality contents between them with up to 10km

coverage.

We then introduce a shared disk to connect different loops and relay a requested

program from one to the other, which is a key system component with multiple interfaces

in order to organize a multiple-loop architecture, working as a bridge. We propose

the multiple-loop architecture using the shared disks by comparing possible topology

designs via thorough analyses and extensive simulations. Finally, we determine which

topology design is the most desirable in terms of scalability, supporting high-quality

content sharing and distribution effectively at a low cost.

1.2.2.2 An Efficient Storage Scheme

Once the multiple-loop architecture is deployed as a network infrastructure, PVR

users can share other PVRs’ contents without a limitation on the number of PVR users

in the network. Ideally, the operations of each PVR should be under its own control by

its user in the proposed multiple-loop architecture. Users can therefore arbitrarily store

or remove any programs on their PVRs independently of how other PVRs are storing

or deleting the same program. As the network architecture is extended with added

loops, i.e., as more PVRs are added, however, the storage of popular programs tends

to be duplicated in many PVRs, thereby wasting the storage space that could otherwise

be used to store additional TV programs. This implies that we should extend program

storage hours by reducing duplicated storage of programs among PVRs’ local storage

and network disks in the system.

20

In order to devise an efficient storage scheme, we need to take advantage of PVRs’

access pattern to popular programs, i.e., which program is more of less likely to be

accessed by PVRs. In addition, PVRs’ pattern for dynamic liveness also has to be

defined because a PVR’s on/off is at its user’s discretion, so that the PVR can work as a

peer in the network. Furthermore, we need to devise schemes regarding how to direct

incoming programs to either a PVR or a network disk and which program should be

replaced to reduce the redundancy for both PVRs and network disks. Essentially, the

criteria to apply all the placements and the replacements have to be determined for the

efficient use of the whole system storage including PVRs and network disks.

With our proposed schemes, we demonstrate that our architecture can extend

program storage hours significantly by our simulation results. In fact, we have found

the program storage redundancy tends to rapidly increase when more loops and more

PVRs are attached. Therefore, a good scheme should utilize the whole system storage

efficiently while reducing to store popular programs from the whole system point of

views. In this study, we demonstrate that an efficient storage-saving scheme can be

designed to store fewer duplicated programs in the whole system. Via our thorough

simulation, the experimental results reveal that our storage-saving scheme can utilize

the system storage capacities efficiently, while extending the program storage hours by

the average of 67.7%.

1.2.2.3 A Practically Constructible Multiple-Loop Architecture

We have firstly selected the complete graph topology as our multiple-loop

architecture design, mainly considering scalability to accommodate more users in a

given number of loops. Thus, we introduce the concept of total traffic which directly

affects the scalability. Nevertheless, it has a critical limitation on the suggested topology

design in terms of a practical constructibility. That is, although the complete graph shows

the best scalability due to the least total traffic by 1-hop reachability to every loop, it

seems impractical, especially when many more loops are deployed or the distance

21

between 2 loops is very huge. In addition, it is inevitable that the size of modified loop

should become larger in order to link a shared disk when it is placed between any 2

loops. In other words, the insertion of shared disk increases the modified loop size

which results in the worse system performance and cost.

Nevertheless, the suggested topology requires too many shared disks, just for the

topology connectivity. Specifically, if there are n loops in the architecture, the system

demands at least nC2 shared disks just for the system organization, thereby making it

less likely to be able to construct a real network architecture.

Therefore, we present a practically constructible multiple-loop topology which

considers both the system performance and cost. We firstly describe the minimum

spanning tree (MST) topology for the system cost. However, it shows unacceptable

system performance since it is a tree-based topology where the traffic load is consecrated

on the selected fixed-path every time. Consequently, we add graph characteristics to the

MST topology so that the load distribution can be achieved for satisfactory system

performance. We finally describe how to configure our proposed topology, what

characteristics it has, and how effectively to expose its superiority in terms of both

performance and cost.

1.3 Technical Background

1.3.1 Multimedia-enabled Small Area Network

It is not an easy task to deliver high-quality multimedia content from a content

server to an end user via the Internet, especially when the geographical location

between them is very far. In fact, the Internet is fundamentally designed for a best-effort

service to transfer data with unreliable and limited network bandwidth, which is not

quite suitable for the multimedia services. Hence, in order to support the conventional

server/client network architecture to transmit multimedia data on the global network

such as the Internet, multimedia-enabled small area networks have been studied in

[40, 62, 71, 76].

22

In fact, once a small area network can support a multimedia service to its users,

there are several performance advantages including various content providing channels

and a decrease of network load since the small area network can work as a proxy

server [47, 57, 84]. However, the proxy server functions mainly discuss how to

reduce the number of outside requests and how to absorb the request in its network

by cache/prefetch schemes.

Our proposed system further adds P2P functions within the network architecture

in addition to the basic functions of a small area network. That is, the residents in the

network cannot only serve their contents to their neighbors as content providers but they

can also retrieve their target programs from others, thereby providing more system load

distribution and reliable content sharing.

1.3.2 Fiber Channel Arbitration Loop

Fiber Channel provides an interface to a pair of cables that can connect a computer

to all other computers and shared peripherals it might need to reach, at data rates

up to 1 Gbps. It has an extremely flexible interconnection topology, with definitions

for point-to-point, switched, and loop connection topologies. The architecture defines

mappings of Fiber Channel data transport to Upper Level Protocols (ULPs), such as the

Internet Protocol (IP), the Small Computer Systems Architecture Standard (e.g., SCSI)

for high-speed disk attachment, and so on. With the Fiber Channel, it is possible to have

an IP connection, disk and storage peripheral access, and a link to an ATM switch, all

operating over a single network card with a pair of attached cables, each of which is

transmitting data at more than 1 Gbps [7, 15, 55, 61].

One of the FC-AL fabric topologies is the Fiber Channel Arbitration Loop (FC-AL)

that is emerging to be employed in the Storage Area Network (SAN) architecture

[4, 21, 44, 74]. In the FC-AL topology, the outgoing and incoming fibers to a port are

split off to attach to different remote ports, such that the aggregation of fibers forms a

unidirectional loop which passes through every port exactly once. The general picture of

23

traffic generation on this topology is that a single port arbitrates for usage of the entire

loop. Once it obtains loop access, it opens up communications with another port on

the loop, and then transmits normal Fiber Channel traffic at the full guaranteed link

bandwidth until it is done, when it releases the loop for usage by another port to begin

another round of full bandwidth communication.

One notable advantage of FC-AL is that it provides fairness to all attached devices.

Each device participates in the arbitration in order to access to the Fiber Channel, and

thereby no device suffers starvation due to the arbitration protocol. In addition, Fiber

Channel can cover up to 10km distance which is highly appropriate to organize a small

area network such as a community or a campus.

With these advantages, there has been research to organize a multimedia-enabled

network architecture using FC-AL [12, 13, 22]. However, these systems are mainly

focused on the feasibility and performance attainability, not aiming for a real resident

network architecture. We have investigated a further consideration for the actual

network infrastructure. In other words, we are focusing how to make it feasible to be

scalable so that many more user can be supported, by overcoming the intrinsic FC-AL

accommodatable limitation. Thus, we consider an effective system design in terms of

topology, and furthermore we develop a practically constructible topology design which

makes a balance between system performance and cost.

1.4 Outline of Dissertation

The remainder of this dissertation is organized as follows:

Chapter 2 describes our proposed FC-AL multiple-loop architectures for small area

networks. We analyze various topology types, (e.g., complete, ring, edge-added, and

complete topology) to determine the better architecture design in terms of scalability,

i.e., the number of attachable devices. With thorough investigation and extensive

simulations, we reveal that the complete topology design provides the most scalability,

and thereby is most appropriate for the multiple-loop network architectures.

24

Chapter 3 proposes storage-saving schemes based on our multiple-loop architectures,

which utilize people’s viewing patterns, i.e., the popular programs tend to be watched

much more, thereby storing same programs redundantly. Therefore, we propose efficient

storage-saving schemes which try to store fewer program copies in order to reduce the

redundant copies of same programs.

Chapter 4 explains a practically constructible architecture solution which tries to

find a balance between system performance and cost. In fact, the CG topology can

be impractical when the number of loops becomes large. Thus, an minimum spanning

tree-based graph topology, called MSG, is devised which is derived from an MST

topology but also reflects the CG topology’s advantages. The MSG then reveals its

superiority by showing a constant average loop size as well as an acceptable total traffic

and reject ratio compared to both GC and MST.

Chapter 5 concludes this dissertation regarding all the topics and presents our

research directions for future work.

25

CHAPTER 2A SCALABLE PVR-BASED CONTENT SHARING ARCHITECTURE

2.1 Motivation

In this chapter, we focus on developing a novel multiple-loop architecture capable

of supporting a scalable community network, as one of small area networks, in an

economical way without using fiber channel switches.

In addition, a personal video recorder (PVR) is an electronic home appliance that

can record broadcast TV programs onto its embedded hard disk in a digital format

[52]. Unlike the conventional video cassette recorder (VCR), it uses a hard disk as

a storage medium rather than a tape. However, it still supports advanced features

including electronic program guide (EPG) and time-shifting (e.g. to pause live TV), in

addition to conventional VCR-like operations, such as fast forward, rewind, pause, and

play. Remarkably, the PVR’s time-shifting functionality has a major effect on people’s

TV watching patterns. That is, the time-shifting feature allows people to become

independent of a TV broadcasting time. Moreover, they can even pause a live TV

program or skip some parts of a live TV show due to the time-shifting functionality.

Furthermore, a PVR has the capability to be connected by a broadband network,

thereby serving as a network component to share stored TV programs with other PVRs.

This feasibility of a PVR-based P2P network has been investigated and tested in the

“Share-it” project [49] and the “ShareTV” by NDS [35].

Therefore, we adopt this PVR as a multimedia device in each home, so that

each user can not only enjoy various multimedia services but our system can also be

organized for a P2P network using each user’s PVR. That is, our system assumes that

each user has a PVR so that he can store his TV watching TV contents that can be

retrieved by other users in the proposed network architecture which serves as a P2P

network.

26

In our proposed architecture, each loop thus connects all PVRs to the loop as a

high-speed interconnecting network. Then, PVRs equipped with network devices can

share TV programs stored on their hard disks with others as a peer as in a P2P network,

especially within the community network.

To connect multiple loops in our architecture, we introduce shared disks that are

responsible for relaying TV programs between loops like bridges. With shared disks,

multiple loops can be configured by various types of topologies in order to construct a

community network. To see which topology can provide the best scalability in terms of

the total number of devices that can be attached to all loops, we analyze four possible

types of topologies: linear, ring, edge-added, and complete topology where a node

and an edge represent a loop and a shared disk, respectively. The four topologies are

classified according to how many adjacent edges each node has.

As the number of the shared disks increases, the total number of devices attached

to all loops decreases because they are attached to more than one loop at the same

time. Thus, we need to reduce the total number of shared disks to provide better

scalability. We have observed that the system’s total loaded traffic affects the required

number of shared disks. In addition, the total traffic depends on the system’s topology

design, i.e., which topology is employed for the whole system’s configuration design.

Ultimately, the system’s topology design determines the necessary number of shared

disks to organize an effective multiple-loop architecture. This is because each topology

generates a different number of hops to reach a destination loop from a source loop.

In order to see how each topology affects the total traffic, we analyze four different

topologies. The analysis reveals that the complete topology has the least total traffic

among four topologies. The reason for this is that any loop can always reach another

loop within a minimum number of hops, i.e., one hop. We also examine the impact of the

number of interfaces per shared disk on the system scalability.

27

The simulation results show that the complete topology has the best scalability

among four possible topologies in terms of the number of devices attached to the

system. It is also shown that the system scalability is not significantly affected by

the number of interfaces per shared disk. It can be therefore seen that our proposed

architecture can adaptively increase the community network simply by adding loops

without using expensive fiber channel switches even though community size continues

to grow.

The remainder of this chapter is organized as follows: Section 2.2 describes related

work. Section 2.3 proposes a scalable TV content-sharing architecture including

shared disks and directory servers. Section 2.4 analyzes the impact of topology

types of a community network and the number of interfaces per disk on the system

scalability. Section 2.5 presents extensive simulation results. Finally, Section 2.6 offers

summarizations.

2.2 Related Work

The design issues pertinent to organizing a community network have been analyzed

in [6, 68], but they have focused on how to organize a community network rather than

how to construct a scalable architecture for a community network including various types

of topologies.

In addition, PVRs’ impact on TV viewing patterns has been described in [45]

and various functionalities of PVRs have been studied in [43, 83]. However, they

concentrated on PVR-specific applications such as time-shifting and video-rate

adaptation.

In the meantime, the fiber channel has been proposed as one of the standards

to organize the storage area network [34]. Thus, its feasibility and capability have

been studied as a high-speed network in [18]. Additionally, the FC-AL-based storage

systems have been studied for multimedia server architectures in [13, 22]. Moreover, the

28

multimedia content sharing architecture with PVRs has been introduced in [10, 49], but

they do not address the scalability issue of the system.

When organizing network topologies, design issues have been introduced in

[11, 33, 48]. However, they studied autonomous systems or clusters as target systems.

A The structure of an FC-AL single loop

B A community network for a large number of devices

Figure 2-1. Overall TV content distribution architecture

29

2.3 A Scalable TV Content Sharing Architecture

In this section, we describe a scalable TV content sharing architecture for a

community network. We first explain the FC-AL single loop architecture and then

propose a scalable multiple loop architecture using shared disks for sharing HD-quality

TV contents among PVRs.

2.3.1 Single Loop Architecture

Fig. 2-1A illustrates a structure of an FC-AL single loop that can be constructed in a

small area. In order to provide high-quality content sharing in real time among PVRs, it

is essential to adopt a high speed network as an interconnecting infrastructure. The fiber

channel (FC) has emerged as a leading technology due to its several advantages. First

of all, FC can provide high bandwidth, i.e., currently 800Mbps, and large coverage, i.e.

10km, which are very suitable to organize a community network where a large number

of PVRs are involved. In addition, the FC-AL employs a fairness arbitration algorithm

that can guarantee there is no starvation among all attached devices. Therefore, we

employ FC-AL technology to connect all system components within a community

network [38].

Once PVRs become equipped with network devices, they can share contents stored

on their hard disks with others. In other words, each PVR can work as a peer as in a

P2P network, especially within a community network. The community members can

share the high-quality TV contents via the PVRs that are connected to the FC-AL. PVRs

record live TV programs that users want. They also continue to record broadcast TV

programs which users have watched for a given duration so that users can time-shift the

programs.

As mentioned above, however, the single loop can accommodate only 127 devices

because the FC-AL standard allows a 7-bit address space [34]. In other words, it is

not suitable as a community network if the community has more than 127 consumer

devices. Therefore, it is essential to extend the community network in an economical

30

way without using fiber channel switches that are very expensive and likely to become

more load-concentrated.

A Two and Three loop configuration

B Four and Five loop configuration

Figure 2-2. Examples of multiple loop architectures

2.3.2 Multiple Loop Architecture

We develop a scalable multiple loop architecture that is able to accommodate a

large number of devices in a community network as shown in Fig. 2-1B. To extend

the single loop architecture to the multiple loop architecture, we employ two additional

system components: one is a shared disk and the other is a directory server.

• Shared disk (SDisk)

31

In the multiple loop architecture, we need a bridging mechanism between loops.

The shared disks are used to connect two distinct loops and relay the requested

program from one loop to the other. Since the FC standard allows multiple

interface devices for such reasons as durability, we can implement the shared

disks without modifying FC protocols [34]. Since disks attached to the FC-AL are

at least dual-interfaced, we can connect each interface to one of multiple loops.

• Directory server (DS)

The directory server coordinates the content sharing between PVRs within a loop

and among loops. Located in every loop, it keeps collecting and exchanging the

information on which PVRs are storing which programs. Based on this information,

it can perform the scheduling of requests for content sharing.

Fig. 2-2 illustrates our proposed multiple loop architecture with two, three, four,

and five loop configurations. Note that each loop represents a single loop architecture

shown in 2-1A. It can be seen that the shared disks work as bridges between loops. In

order to effectively extend the architecture with a given number of loops, it is important to

determine how many shared disks should be employed between loops. The more disks

are designated as shared disks, the worse scalability we may achieve. Moreover, it can

be a performance bottleneck when they are overloaded due to improper design.

2.4 Design of a Scalable Loop Topology

In this section, we present how to obtain better scalability for the multiple loop

architecture with a given number of loops. As mentioned above, the FC-AL single

loop can attach at maximum 127 devices. In the meantime, the shared disks worsen

the scalability because they are attached to more than one loop at the same time.

Thus, we can obtain the total number of devices that can be attached to a multiple loop

architecture using shared disks as follows:

127 × (number of loops) − (total number of shared disks) (2–1)

32

A Linear and Ring topology

B Edge-added and Complete topology

Figure 2-3. Examples of topology with six loops

From Eq. 2–1, it is clear that we should reduce the total number of shared disks to

provide better scalability. As mentioned above, we have observed that the total traffic

loaded on all shared disks depends on the type of topology constructing a multiple loop

architecture. This is because the required number of hops to transfer data between a

specific pair of loops differs according to each topology.

In order to see how each topology affects the total traffic loaded on all shared disks,

we illustrate four different topology configurations with six loops as shown in Fig. 2-3.

Since the configurations with a larger number of loops are much harder to illustrate in

figures, we use the six-loop configurations for detailed analysis of each topology in this

section.

33

Fig. 2-3A and 2-3B show a linear, ring, edge-added, and complete topology,

respectively. A node and an edge represent a single loop and a shared disk, respectively.

We compare these topologies in terms of the total traffic with a given number of

loops. We use dual-interfaced shared disks for the convenience of comparison. We

then examine the impact of the number of interfaces per shared disk on the system

scalability.

2.4.1 Linear Topology

The simplest way to configure a multiple-loop architecture with a given number of

loops is to use a linear topology as shown in Fig. 2-3A. In the linear topology, it can be

seen that the number of edges is less by one than the number of nodes.

In our multiple-loop architecture, when a PVR requests a program, the system first

tries to find the requested program within the local loop where the requesting PVR is

located. If the program is not found in the local loop, i.e., the program is stored only in

other loops, the requesting PVR must receive the program from one of other loops via

all shared disks located between the two loops. The routing is determined based on the

total number of hops between a source and a destination loop, i.e., the shortest path

is chosen among possible paths. It is assumed that the programs are stored evenly

among PVRs and the popularity of programs is uniform so that the amount of data traffic

between all pairs of loops can be fairly evaluated.

In the linear topology, it is obvious that the edges closely located to a center node

handle more traffic because they tend to be more involved in relaying programs between

loops. For example, in Fig. 2-3A, the center edge (i.e., edge c) handles the most traffic

among all five edges due to the task of relaying programs. The second and fourth edge

(i.e., edge b and edge d) handle the second most traffic for the same reason. The first

and the fifth edge (i.e., edge a and edge e) have the least traffic. Specifically, when

denoting m a relative unit of traffic, the very left node (i.e. node 1) generates traffic as

much as (5/5)m, (4/5)m, (3/5)m, (2/5)m, and (1/5)m to edge a, edge b, edge c, edge d,

34

Table 2-1. Traffic on each edge in a six loop linear topology

EDGE A EDGE B EDGE C EDGE D EDGE E

Node 155

m45

m35

m25

m15

m

Node 215

m45

m35

m25

m15

m

Node 315

m25

m35

m25

m15

m

Node 415

m25

m35

m25

m15

m

Node 515

m25

m35

m45

m15

m

Node 615

m25

m35

m45

m55

m

Total Traffic 2m165

m185

m165

m 2m

where m is a relative unit of traffic.

and edge e, respectively. Similarly, node 2 generates traffic as much as (1/5)m, (4/5)m,

(3/5)m, (2/5)m, and (1/5)m to edge a, edge b, edge c, edge d, and edge e, respectively.

Table 2-1 shows the total traffic on each edge in the linear topology with six loops. The

total traffic on edge a, edge b, edge c, edge d, and edge e are 2m, (16/5)m, (18/5)m,

(16/5)m, and 2m, respectively. The total traffic on all edges is computed as 14m simply

by summing the traffic on five edges.

In general, we can derive the total traffic in the linear topology according to the

number of loops as follows:4 − 4

(n − 1)×

n−12∑

k=2

(k2 − n × k)

× m, n : odd & n ≥ 5

4 − 4

(n − 1)×

n−22∑

k=2

(k2 − n × k) +n2

2 × (n − 1)

× m, n : even & n ≥ 6

(2–2)

where n is the number of loops and m is a relative unit of traffic.

35

2.4.2 Ring Topology

Since the ring topology has a circular connection among loops as shown in Fig.

2-3A, all the nodes have two edges and we cannot designate any edge as a center

edge. A node can reach any other node within as many hops as half the total nodes.

Table 2-2. Traffic on each edge in a six loop ring topology

EDGE A EDGE B EDGE C EDGE D EDGE E EDGE F

Node 135

m25

m15

m 015

m25

m

Node 225

m35

m25

m15

m 015

m

Node 315

m25

m35

m25

m15

m 0

Node 4 015

m25

m35

m25

m15

m

Node 515

m 015

m25

m35

m25

m

Node 625

m15

m 015

m25

m35

m

Total Traffic95

m95

m95

m95

m95

m95

m

In order to find out how much traffic is generated in the ring topology, we first obtain

the traffic of each edge. In Fig. 2-3B, node 1 generates traffic as much as (3/5)m,

(2/5)m, (1/5)m, (1/5)m, and 2/5)m to edge a, edge b, edge c, edge e, and edge f,

respectively. Note that when there is a tie in determining the routing path in terms of

the number of hops, we choose the loop in the clockwise direction. Thus, edge d does

not have any traffic generated by node 1 due to this tie-breaking policy based on the

shortest path routing. Similarly, we can calculate the traffic on other edges as shown in

36

Table 2-2. We can thus generally formulate the traffic of each node as follows: 2

(n − 1)×

n−12∑

k=1

k =n + 1

4

× m, n : odd

2

(n − 1)×

n−22∑

k=1

k +n/2

2 × (n − 1)

× m =

[n2

4(n − 1)

]× m, n : even

(2–3)

With Eq. 2–3, we can obtain the total traffic in the ring topology simply by multiplying the

traffic of each edge by the number of edges as follows:[

(n + 1)4

× n]× m =

[n(n + 1)

4

]× m, n : odd

[n2

4(n − 1)× n

]× m =

[n3

4(n − 1)

]× m, n : even

(2–4)

2.4.3 Complete Topology

The complete topology represents a simple graph where every pair of distinct

nodes is connected via a distinct edge. Thus, every node can have all other nodes as

adjacent nodes. In other words, a node can reach any other node with only one hop. In

the complete topology, there are thus nC2= n(n-1)/2 edges in total because all nodes are

paired. Each node has (n − 1) adjacent edges because each node has all other nodes

as neighbors.

First, we derive the traffic of each edge. In Fig. 2-3B, each node generates (1/5)m

traffic to all other adjacent edges. Since each edge receives the same traffic from two

connected nodes, the traffic of each edge is (2/5)m.

Thus, we can generally derive the traffic of each edge in the complete topology as

follows: [2

(n − 1)

]× m (2–5)

Since the total number of edges in the complete topology is n(n − 1)/2, we compute

the total traffic for all edges by multiplying the traffic of each edge by the total number of

37

edges as follows: [{2

(n − 1)

}× m

]× n(n − 1)

2= n × m (2–6)

The total traffic of the complete topology with six loops in Fig. 2-3B is 6m. Compared

to the total traffic of the linear and ring topology with six loops in Fig. 2-3A, amounting

to 14m and 10.8m, respectively, it can be seen that the complete topology has the least

traffic.

2.4.4 Edge-Added Topology

We have analyzed the total traffic of the linear, ring, and complete topology. Note

that each node has two and (n − 1) adjacent edges in the ring and complete topology,

respectively. However, it is also necessary to examine other topologies where each node

has 3 to (n − 2) adjacent edges to compare all possible topologies. In this chapter, the

ring-based topologies where each node has 3 to (n − 2) adjacent edges are edge-added

topologies as shown in Fig. 2-3B.

However, it is hard to derive general formulae for the total traffic in the edge-added

topologies due to their irregularities. Thus, we have performed extensive simulations to

investigate the total traffic of all the topologies where each node has 2 to (n−1) adjacent

edges including ring and complete topology. The simulation results will be presented in

Section 2.5.

2.4.5 Multiple-Interfaced Shared Disks

In the previous section, we analyzed the linear, ring, edge-added, and complete

topology that are based on dual-interfaced shared disks. In the complete topology, at

least nC2 shared disks are required to connect all pairs of loops. Thus, as the number of

loops increases, the required minimum number of shared disks increases more rapidly,

proportional to n(n − 1)/2. In this section, we investigate whether multiple-interfaced

shared disks can provide better scalability than dual-interfaced shared disks can. In

other words, we examine whether or not the smaller number of multiple-interfaced

shared disks can maintain the same connectivity as the larger number of dual-interfaced

38

shared disks can. As mentioned above, this is feasible since the fiber channel standard

allows multiple fiber channel-interfaced hard disks.

Table 2-3. m values in the complete topology

NUMBER OF INTERFACES PER SHARED DISK

2 3 4 5 6 7 8 9 10

3 loops 3 1 N/A N/A N/A N/A N/A N/A N/A

4 loops 6 3 1 N/A N/A N/A N/A N/A N/A

5 loops 10 4 3 1 N/A N/A N/A N/A N/A

6 loops 15 6 3 3 1 N/A N/A N/A N/A

7 loops 21 7 4 3 3 1 N/A N/A N/A

8 loops 28 11 6 4 3 3 1 N/A N/A

9 loops 36 12 7 4 3 3 3 1 N/A

10 loops 45 17 8 6 4 3 3 3 1

We first derive the minimum number of multiple-interfaced shared disks in the

complete topology where any node can reach any other node with only one hop as

follows:

M =

n ×⌈

(n − 1)(k − 1)

⌉

k

(2–7)

where n, k , and M represent the number of loops, the number of interfaces that one

shared disk supports, and the minimum required number of shared disks, respectively.

The complete topology comprising dual-interfaced shared disks is a special case

where k = 2 in Eq. 2–7. Table 2-3 illustrates M values when varying the number of loops

and interfaces per disk.

Fig. 2-4 shows three examples of complete topologies using multiple-interfaced

shared disks. Fig. 2-4A shows the complete topology with four loops and three

three-interfaced shared disks. In this topology, shared disk a, b, and c connect loop

1/2/4, 1/2/3, and 2/3/4, respectively. Fig. 2-4A shows the complete topology with eight

loops and four five-interfaced shared disks. Similarly, four disks connect eight loops

39

A n=4,k=3,M=3 and n=8,k=5,M=4

B n=10,k=10,M=1

Figure 2-4. Examples of complete topologies using multiple-interfaced shared disks

using five interfaces so that every pair of loops can be reached with only one hop. Fig.

2-4B shows the complete topology that can be constructed with only one shared disk.

This is possible because the shared disk has ten interfaces that are able to connect ten

loops at a time. We can thus derive the traffic of each shared disk with k interfaces as

follows: [(k − 1)(n − 1)

× k]× m (2–8)

40

Then we can also obtain the total traffic of the complete topology with k-interfaced

shared disks using Eq. 2–7 and 2–8 as follows:

[{(k − 1)(n − 1)

× k}× m

]×

n ×⌈

(n − 1)(k − 1)

⌉

k

= n × m (2–9)

It can be seen from Eq. 2–6 and 2–9 that the total traffic of the complete topology

with multiple-interfaced shared disks is same as that of dual-interfaced shared disks.

This may imply that the total traffic is not affected by which type of shared disk is

employed. However, note that we may need more shared disks as the traffic of each

edge becomes heavier even though only one shared disk can connect all loops as

shown Fig. 2-4B. Since each shared disk has a limitation on handling traffic, we should

also take processing capability of each shared disk into account to estimate the real

scalability. In the next section, we will show the actual required number of shared disks

considering both the processing capability of each shared disk and the traffic loaded on

it.

Table 2-4. Simulation parameters

CATEGORY PARAMETER VALUE

FC-AL

data transfer rate 400 MB/s

propagation delay 3.5 ns/meter

per node delay 240 ns

Disk

capacity 300 GB

cache 32 MB

data transfer rate 29 ∼ 65 MB/s

seek time 4.7 ms

rotational latency 3.0 ms

41

2.5 Experimental Evaluation

To validate the effectiveness of our proposed architecture in terms of scalability, we

have performed extensive simulations for various types of topologies while varying the

number of loops and interfaces per disk.

It is assumed that each TV program has HD quality of 19.4 Mbps playback rate

and is 60 minutes long. We employ dual-interfaced shared disks between two distinct

loops in order to organize a multiple loop architecture unless otherwise indicated. We

also vary the number of interfaces per shared disk to show the impact on the overall

scalability. The programs are distributed evenly among loops and the probability that

each program is requested is uniform so that the amount of data traffic between each

pair of loops can be fairly compared. The request rate of program playback follows a

Poisson distribution. Each available PVR issues requests for different programs every 60

minutes. It is also assumed that 126 PVRs are connected to each loop and half of them

on average are available at a specific time. Note that the routing between two loops is

determined by the number of hops and the tie-breaking policy as mentioned above. The

details of parameters used for disks and FC-AL are also illustrated in Table 2-4.

The simulation results demonstrate that the complete topology provides the best

scalability, which supports our analysis presented in the previous section.

2.5.1 Scalability Comparison among Different Topologies

In this section, we evaluate the scalability degree of up to eight topologies (i.e.,

when the number of nodes is ten) by measuring their total traffic in terms of the

number of programs being serviced. Since the minimum number of nodes to form a

ring topology is three, we vary the number of nodes up to ten, starting from three. Since

the number of edges per node of a ring topology is two, we keep adding one adjacent

edge to each node to construct edge-added topologies until it is (n − 1) , i.e., it becomes

a complete topology. Note that, due to the irregularities of edge-added topologies, when

adding one edge to each node, it is not possible to strictly keep the numbers of edges

42

adjacent to nodes all the same. Thus, we try to distribute additional edges among nodes

as evenly as possible so that the average number of edges adjacent to nodes can be

maintained consistently depending on the number of added edges.

Figure 2-5. Total traffic of different topologies according to the number of loops

Fig. 2-5 shows the total traffic according to topology type while varying the number

of nodes (i.e., loops) from three to ten. The topology type is determined by the number

of average edges connected to each node. For instance, in the case of n=10, the

topologies with two and nine edges per node denote the ring and complete topology,

respectively. The six topologies with three to eight edges per node represent all possible

edge-added topologies. It can be seen that, as the number of edges per node increases,

the total traffic on each topology decreases. For example, in the case of n=10, when

each node has two edges, i.e., the ring topology, the total traffic is approximately 1,579

programs on average. On the other hand, when each node has nine edges, i.e., the

complete topology, the total traffic is on average only 577 programs. This trend is

consistent with all other topologies with different numbers of nodes. The reason for this

43

Figure 2-6. Number of shared disks required by different topologies according to thenumber of loops

Figure 2-7. Number of attached devices of different topologies according to the numberof loops

44

is that each topology generates a different number of hops to transfer data between

a source and a destination node. Note that, in the complete topology, any PVR can

receive a program from any other PVR in another loop with only one hop (i.e., one

shared disk). Thus, we can see that the complete topology generates the least total

traffic among all possible topologies to construct multiple loop architectures using

shared disks.

It is clear that the topologies with less traffic require fewer shared disks. This implies

that we can achieve better scalability because shared disks are attached to more than

one loop at the same time. Fig. 2-6 illustrates the required number of shared disks

for each topology type while varying the number of nodes. In the case of n=10, the

complete topology requires only 135 shared disks while the ring topology requires 320

shared disks. Note that the number of shared disks per edge in the ring topology is

much larger than that in the complete topology even though the number of edges in the

ring topology is smaller.

Fig. 2-7 shows the total number of attached devices for each topology. As expected,

the complete topology can support the greatest number of attached devices among all

the topologies to construct a multiple loop architecture. For example, in the case of n=

10, the complete topology can attach 1,125 devices while the ring topology supports

940 devices, which indicates 19.68% improved scalability. It can be also seen that the

complete topology outperforms all the other topologies for all the cases with different

numbers of nodes.

2.5.2 Impact of the Number of Interfaces per Disk

Fig. 2-8 shows the impact of the number of interfaces per shared disk in the

complete topology while varying the number of loops. It can be seen that the difference

in the number of attached devices is negligible for all the numbers of loops from two to

ten. For example, in the case of n= 10, the number of attached devices is 1,125 when

employing the dual-interfaced shared disks while the system with ten interfaced shared

45

Figure 2-8. Number of attached devices in the complete topology according to thenumber of interfaces per disk

disks supports 1,144. This indicates only 1.69% difference in scalability even though the

average processing utilization of shared disks is quite a bit lower with dual-interfaced

disks than with ten interfaced disks. This is because, as the number of interfaces

increases, the overhead including loop arbitration increases rapidly. Fig. 2-9 illustrates

the increased average one-block (512KB) processing time as the imposed overhead,

according to the number of interfaces per shared disks in the 10-loop architecture.

Thus, we can see that more interfaced disks do not contribute significantly to the system

scalability.

2.6 Summary

In order to deliver high-quality multimedia content, it is essential to support high

bandwidth in a network area. To address these requirements, a content-sharing

architecture based on an FC-AL loop has been proposed. Since a single loop allows

only 127 attachable components due to its 7-bit address space, it is not suitable for

a network area in a high-density community area. Therefore, as an alternative to

46

Figure 2-9. Average block overhead time according to the number of interfaces per disk

expensive switches for a network area, we have proposed a scalable multiple-loop

architecture using shared disks that relay the requested programs from one loop to the

other.

In order to determine which topology has the best scalability, we analyzed the

total traffic for four possible topologies such as linear, ring, edge-added, and complete

topology. Through analysis and extensive simulations, we showed that the complete

topology has the best scalability because it requires the fewest number of shared disks

by generating the least traffic among the four topologies. We also showed that the

system scalability is not affected by the number of interfaces per disk significantly.

We showed that ten loops connected via shared disks can serve more than 1,120

devices without employing switch devices. Our proposed architecture is therefore

expected to be a very scalable community network capable of supporting many more

PVRs with the increased number of loops since the fiber channel standard technically

allows up to 10 km coverage.

47

CHAPTER 3A STORAGE SAVING SCHEME TO SHARE HD-QUALITY CONTENT

3.1 Motivation

In the previous chapter, we have developed a multiple-loop FC-AL-based architecture

for a community network with the concept of shared disks, which play a role for bridges

to connect the different loops [14]. With the shared disks in the system, communication

between the hosts/disks in different loops becomes feasible. Therefore, our design focus

on the system is shifted to make sure the given storage space is effectively utilized.

Unlike the conventional SCSI-based servers that manage the video file allocation

issues in the dominating role, FC-AL-based P2P networks provide the system more

flexibility in that every PVR can equally access all the disks on the networks. Thus

content-sharing among the local disks in PVRs is quite feasible, which allows the system

to be more dynamic for using the whole system storage compared to the server-only

system. Thus, the PVR-based FC-AL architecture in general should not require as many

copies of the programs as the conventional SCSI-based servers/systems. Therefore,

PVR-based FC-AL systems may reduce the required number of copies of programs

significantly and effectively.

We believe that the operations of each PVR should be under the control of its

user. Users can therefore arbitrarily store or remove any programs on their PVRs

independently of how other PVRs are storing or deleting the same program. As the

community network can be extended with added loops in order to accommodate more

PVRs, however, the storage of popular programs tends to be duplicated in many PVRs.

This implies that the community network overall would waste the storage space that

could otherwise be used to store additional TV programs. In addition, PVRs can support

the feature of auto-record with their pre-assigned local storage, by which the PVRs can

function for time-shifting service like TiVo [5]. Consequently, this implies that we can

48

extend program storage hours by reducing duplicated storage of programs with PVRs’

auto-record storage within a community network.

We therefore analyze the problem, and propose novel schemes to extend program

storage hours in a community network. These schemes include the algorithm to

determine the minimal number of copies of the associated programs, and storage-saving

algorithms for both PVRs and network disks. These schemes jointly consider the

storage system design issues including access skew among programs and PVRs’

limited availability. We then perform extensive simulations while considering the

various system parameters in order to demonstrate the effectiveness of our proposed

architecture and algorithms. The simulation results successfully demonstrate that our

architecture can extend program storage hours significantly.

The remainder of this chapter is organized as follows: Section 3.2 describes

previous work related to our chapter. Section 3.3 explains the overall architecture for our

proposed community networks, especially FC-AL multiple loop architecture, to deliver

high-quality multimedia data. Section 3.4 explains our storage saving schemes for PVRs

and network disks. Section 3.5 illustrates extensive simulation results. Finally, Section

3.6 offers a summarization.

3.2 Related Work

In order to provide broad bandwidth stability and reliability, the fiber channel has

been proposed as one standard to organize a storage area network [34].Thus, its

feasibility and capability have been studied as a high-speed network [18, 19] and its

attainable performances have been analyzed [30, 60]. There has been research on

FC-AL-based multimedia server architectures using FC-AL storage systems [12, 13, 22].

There has also been literature on PVRs that can offer entertainment services at

home such that the end-users can share what they are storing in their own PVRs in

P2P networks [3, 36, 49, 79]. In addition, high-quality streaming services based on

content distribution networks have been exploited [9, 16, 53] although those services

49

have focused on unidirectional distribution from servers to end-users, rather than on

bi-directional content-sharing among users. A novel FC-AL-based content sharing

architecture in a residential area has been proposed so that HD-content sharing among

PVRs can be provided [39]. However, this system only focused on the feasibility of the

system architecture with the single-loop FC-AL configuration. Thus, it has limitations as

a community network infrastructure because it can accommodate only a limited number

of community members.

In addition, as the network and multimedia technologies have advanced, the

multimedia streaming has become much more feasible in a variety of services such as

P2P sharing. Multimedia streaming over P2P network can be categorized in three types:

pure streaming, implicit duplication, and explicit duplication as described in Section 1.1.

To date, there have been introduced several P2P multimedia applications such

as Joost [51], PPLive [? ], TvAnts [63], Cabos [65], Kiwi Alpha [54], Shareaza [50],

BitTorrent [41], BitComet [46], Azureus [67], and so on. The pure streaming types are

Joost [51], PPLive [? ], and TvAnts [63]. Next, the implicit duplication types are Cabos

[65], Kiwi Alpha [54], and Shareaza [50]. Lastly, the explicit duplication types include

BitTorrent [41], BitComet [46], and Azureus [67].

In our system, we adopt implicit duplication as a P2P content-sharing, so that

the program stored in each PVR can be effectively shared with other PVR users

without suffering a delay or a jitter. In addition, our system can also work as by explicit

duplication method once a whole program is recorded in a PVR until when the program

is deleted.

3.3 TV Content Distribution Architecture for Community Networks

In order to provide content distribution and sharing among community members, it

is important to build an infrastructure to sustain those services as a community network.

In particular, in order to support real time HD-quality content sharing, it is required

50

to provide sufficient storage bandwidth, huge storage capacity and each member’s

willingness to contribute their resources to the community.

In this section, we introduce a novel TV content distribution architecture for a

community network to meet these requirements. We have explained the FC-AL

single-loop community network in section 2.3.1. Then, we describe and review a

scalable multiple-loop architecture using shared disks as the infrastructure of the

community network. The proposed architecture can adaptively increase the community

network simply by adding loops even though community size continues to grow [14].

We therefore develop the scalable multiple-loop architecture for a community

network that has no limitation on the number of attached devices as shown in Fig. 2-1B.

When extending the single loop system to the multiple loop system, however, we need

to devise a bridging mechanism to relay the requested program between different loops.

Thus, we adopt shared disks that connect two distinct loops and relay the requested

program from one loop to the other. Since the FC standard allows multiple interface

devices for such reasons as durability, we can implement the shared disks without

modifying FC protocols.

Fig. 3-1 illustrates proposed multiple-loop architectures in order to support an

infrastructure of a community network, using the shared disks and the directory server

with possible 2-loop, 3-loop, and 4-loop configurations. Note that each loop indicates a

single-loop architecture, as in Fig. 3-1A.

Fig. 3-1A shows the fundamental architecture to extend from single loop to double

loop using shared disks. In this architecture, we can see that the shared disks work like

bridges, which enable the multiple-loop architecture and can be a path between the

loops. Fig. 3-1A illustrates a 3-loop architecture. With more than 3 loops, we notice that

there will be some design issues in terms of how to organize a multiple-loop architecture

with the shared disks. However, in order to organize effectively in terms of accessibility

so that each loop can reach all the other loops without involving more shared disks and

51

A Two-loop and three-loop configuration

B Four loop configuration

Figure 3-1. Examples of multiple loop architectures

more loops, it is desirable to deploy a shared disk between every two loops as in Fig.

3-1A. For example, we can see from Fig. 3-1A that the loop-1 can reach the loop-3 with

only one shared disk between them, rather than passing through the loop-2 with two

shared disks involved. Similarly, we can organize a 4-loop architecture as in Fig. 3-1B.

We can also see that the every loop can reach any other loops via only one shared disk

in Fig. 3-1B.

52

Although this may need slightly more shared disks to connect different loops, it is

clear that the shared disks involved as the routing path between loops should be the

fewest, i.e., one shared disk. Thus, in order to configure a multiple-loop architecture

effectively, this organization is preferable in terms of the routing efficiency since there

will be more routing delays and jitters when more shared disks are involved in the path,

which is not suitable especially for HD-quality multimedia content delivery.

Therefore, with applying this multiple-loop organization, we can construct the

following formula in order to obtain the number of attachable devices with respect to the

given loop number:

(number ofattachable devices) = 127 × n −

n

2

(3–1)

where n is the number of loops.

Now, we can organize the multiple-loop architecture by using the shared disks for

the community network infrastructure. With the proposed multiple-loop architectures, we

can deliver the HD-quality content sharing and distribution among community members,

i.e., PVRs, without limiting the number of community members [14].

3.4 Extending Content Storage Hours

We have proposed a novel TV content distribution architecture for community

networks by configuring the multiple loops and network disks. In order to further utilize

the given system storage capacity, we investigate how to extend TV content storage

hours in a community network. The basic idea is to increase the storage efficiency of a

community network by decreasing duplicated program storage on PVRs and network

disks. We first describe design issues to increase the storage efficiency in a community

network. We then evaluate the impact of duplicated storage of programs and present

the efficient replacement schemes for both PVRs and network disks to extend program

storage hours significantly.

53

3.4.1 Design Issues for Storage Efficiency

In our proposed architecture, the total storage capacity of a community network

depends on the numbers of PVRs and network disks. As the size of a community

network grows, more PVRs can be attached to that community network. Accordingly,

the number of PVRs that store each program also increases. Moreover, in such a

closed system, the number of PVRs storing popular programs will go up rapidly.

Consequently, due to the duplicated storage of programs, the community network

wastes storage capacity that could otherwise be used to store additional programs. Also,

PVRs in a community network have the characteristic of being transient like the peers

in the peer-to-peer (P2P) networks. That is, even though PVRs may be active at any

moment, they can leave the community network at any time. In order to increase storage

efficiency in a community network, we therefore need to address such design issues in

the joint consideration of access skew among programs (because of the differences in

popularity) and the limited availability of live PVRs.

3.4.2 Impact of Duplicated Storage of Programs

It is known that the degree of access skew for popular programs usually follows the

Zipf distribution [1, 42]. Thus, the probability that each PVR records the i th most popular

program among the $n$ programs can be computed as follows:

P i ,αpop =

1/i 1−α

n∑k=1

1/k1−α

(3–2)

where α represents the access skewness degree (i.e., as α increases, the skewness

degree decreases).

To show the impact of storage duplication of programs, Fig. 3-2 illustrates an

example where there are 50 TV channels, i.e., 50 different programs are always being

broadcast. In addition, we set the α to 0.271, which is a typical parameter for popularity

distribution of video rentals.

54

Figure 3-2. Probability of storing each program according to its popularity

We can see from Fig. 3-2 that the first ranked (the most popular) program needs

be stored in 13.3% of all PVRs, while the 50th ranked program needs to be stored in

about 1% of PVRs. In other words, if we deploy 1,000 PVRs, 133 copies of the first

ranked program will be recorded and only 10 copies of the 50th ranked program will

be recorded. This indicates that, if there are too many duplicated copies of popular

programs among PVRs, the community network will waste storage space significantly.

In addition, the conventional file server systems based on SCSI do not share

the interface bandwidth among connected disks since all disks are fully controlled

by the servers [78]. The SCSI-based disks are passive in nature, and only a server

can access them by the SCSI commands. Moreover, the servers are less successful

in getting the program contents from the clients promptly and adaptively compared

to the P2P approach. However, the FC-AL with PVRs enables the system to share

the disk bandwidth. They also have more advantages such as peer-matching and

peer-switching [39], which allow the system to piggy-back on the existing streams.

55

Table 3-1. PVRs’ Contributable Storage in the PVR-based FC-AL system

Sharing Storage Portion of each PVR Program Numbers to Store Additionally

10% 182

20% 364

30% 547

40% 729

50% 912

60% 1094

70% 1276

80% 1459

90% 1641

100% 1824

Furthermore, content-sharing among the local disks in PVRs allows the system to be

more flexible compared to the server-only system for using the whole system storage.

Thus, the PVR-based FC-AL architecture in general does not require as many copies

of the programs as the conventional SCSI-based servers/systems. Table 3-1 explains

that PVRs’ contributable storage space in terms of the number of programs in the

PVR-based FC-AL architecture when assuming half of PVRs are deployed, that half

of them are alive, and each PVR provides the indicated sharing portion to the system,

which is comparable to the passive SCSI-based system. Ideally, PVR-based FC-AL

systems can reduce the required number of copies of programs significantly and

effectively.

In our architecture, a PVR can receive any program stored on other PVRs or

network disks. When a PVR requests a program that more than one alive PVR or the

network disks are storing, it can receive the program from one of the alive PVRs or

network disks via P2P streaming within the community network. Thus, as long as we

can guarantee that sufficient copies of each program are available at any given time, we

56

may remove redundant copies, thereby saving the storage space for additional programs

that can be added later.

3.4.3 Storage Saving Schemes

First, we explain the behavior characteristics of a PVR. That is, each PVR is

only operated by its owner’s orders such as watch, store, or remove. Furthermore,

we assume the PVRs in the system can support the feature of auto-record, which

service has been adopted in TiVo [5]. In our system, the PVRs’ auto-record can work

automatically for time-shifting services, once the storage is set by its user. For example,

if a PVR has 500GB storage and its user sets 10% of storage for auto-record, then

50GB is assigned for storing time-shifting programs. Specifically, once a PVR watches a

program, the program is automatically stored in the auto-record storage of the PVR.

Our scheme has been devised based on these facts. That is, our scheme proposes

to store only a sufficient number of the programs, initially broadcast with a given

existence probability. In other words, if we define a threshold value for a program

existence, we can determine how many PVRs should be alive at least, in order to obtain

the required number of copies for that program with respect to that threshold value.

Given those alive PVRs having the target program, we can determine the minimum copy

numbers of the program as follows:

1 − (1 − Palive)m ≥ threshold

m ≥ log(1−Palive )(1 − threshold)(3–3)

where m is the minimum required number of copies for a program and Palive is the

probability of each PVR’s being alive.

For example, if the existence threshold value is given as 0.9 and the Palive is 0.5,

then we can get the minimum copy number for PVRs, m, as 4 from Eq. 3–3. That is, if 4

alive PVRs each have the target program, our system determines that that program can

be regarded as minimal-copy with respect to the given threshold value and Palive .

57

In addition, our system provides the network disks as the backup storage for the

programs. In order to avoid over-duplicated copies of programs, we can see that it is

sufficient to store a program either as 1-copy at the network disks or m-copy among

the alive PVRs. Therefore, our proposed scheme is fundamentally trying to maintain

minimal-copy of each requested program between alive PVRs and network disks. That

is, in order to save the system storage consisting of network disks and PVR storage,

if the network disks have the target program, then not all PVRs need to store the

corresponding program. On the other hand, if there exist m-copies of the target program

among alive PVRs, then the network disks do not need to store the corresponding

program, either.

Table 3-2. Symbols for replacement algorithms

SYMBOL DEFINITION

pi A program with index i

SiA set of programs stored in a PVR with indexi

SND A set of programs stored in network disks

mThe minimum required copy number to storea program onto alive PVRs with respect tothe given threshold

NiThe number of copies of the program withindex i among alive PVRs

NNDi

The number of copies of the program withindex i among the network disks

Table 3-2 shows the symbols used in our storage saving schemes such as

program placement and replacement algorithms. The program placement algorithm

in Algorithm 3-1 tries to hold at least the sufficient number of programs on behalf of

the whole system. If the PVRs try to store mth copies of a program when the network

disks currently hold that program, the system allows the PVRs to store mth copy but it

58

Algorithm 3-1 Program placement algorithm (input: new program)

1: if ((Ni == m)&&(NNDi == 0)) then

2: Do not store that program to any storage;

3: else if ((Ni < m)&&(NNDi == 0))) then

4: Store that program both PVR and network disk;

5: else if ((Ni == (m − 1))&&(NNDi == 1))) then

6: Store that program to PVR;

7: Remove that program from network disk;

8: else if (Ni == (m − 1)) then

9: Store that program to PVR;

10: else

11: not reachable;

12: end if

13: return

removes the program from the network disks, so that the system can free up the space

for the other program’s placement.

3.4.4 Replacement Schemes

Since every PVR is under each user’s control, it can be difficult to predict what

programs each PVR will record. As mentioned above, due to the skewed demand for

access to the popular programs, there must be storage duplication of the programs

among PVRs. Moreover, PVRs have dynamic behavior characteristics like the ability to

freely leave or join a community network as a peer in P2P networks. In order to avoid

storing redundant copies of each program among many PVRs, we also devise efficient

replacement schemes, which cooperate with the PVRs and the network disks.

The design goal is therefore to maintain at least sufficient copies of each program

on the alive PVRs or the network disks for later access, while also reducing the

excessive redundant copies. We therefore propose replacement schemes for both

59

PVRs and network disks to increase storage efficiency of the community network by

choosing the most appropriate program.

Algorithm 3-2 Replacement algorithm for PVRs (input: new program)

1: if (Si ∩ SND 6= φ) then

2: Find a set of programs belonging to Si ∩ SND ;

3: Choose the oldest program among them;

4: else if ({pi |pi ∈ Si ∩ Ni > 1} 6= φ)) then

5: Find a set of programs belonging to {pi |pi ∈ Si ∩ Ni > 1};

6: Choose the program that has the largest Ni among them;

7: else

8: //there exist only programs that have one copy in the community network

9: Choose the oldest program among them;

10: Move it to network disks;

11: end if

12: Replace the chosen program with new program;

13: return

The replacement algorithm for PVRs determines what program a PVR should

replace with a new incoming program as shown in Algorithm 3-2. The algorithm first

checks whether or not there exist any programs that are also stored on network disks:

i.e., if Si ∩ SND 6= φ. As long as network disks store the programs, PVRs may not need

to store them any longer because the network disks are always available, providing

sufficient disk bandwidth. If so, we choose the oldest program among them, i.e., the

program that had been stored earliest, because the oldest one is less likely to be

accessed in the future. The system will send an automatic message to recommend the

PVR owner remove the program copy with the assurance that the copies on the network

disks will remain available. If there is no program satisfying the above condition, we

check whether there exist any programs that are also stored in other alive PVRs: i.e., if

{pi |pi ∈ Si ∩ Ni > 1} 6= φ. If found, we choose the program that has the most number

60

of copies among a set of the programs satisfying the condition so that this program is

likely to have the most number of redundant copies. Finally, if there are programs that

have the last copy in a community network, we also choose the oldest one among them

but move it to the network disks. The reason for doing this is that we need to maintain

at least one copy of each program within a community network before removing all the

other copies of the program.

Algorithm 3-3 Replacement algorithm for network disks (input: new program)

1: if ({pi |Ni > 0} 6= φ) then

2: Find a set of programs belonging to {pi |Ni > 0};

3: Choose the program that has the largest Ni among them;

4: else

5: //there exist only programs that have one copy in the community network

6: Choose the oldest program among them;

7: end if

8: Replace the chosen program with new program;

9: return

The replacement algorithm for the network disks also addresses which program a

pool of network disks should replace with an incoming program, shown in Algorithm 3-3.

We first check whether there exist any programs that are stored in any alive PVRs: i.e.,

if {pi |Ni > 0} 6= φ. If so, we choose the program that has the most number of copies in

Algorithm 3-2. If not found, it indicates that all the programs stored in network disks have

only one copy within a community network. Thus, we choose the oldest program among

them and replace it with the incoming program.

3.5 Experimental Evaluation

To validate the effectiveness of our proposed architecture and algorithms in terms

of storage efficiency, we have performed extensive simulations while varying several

system parameters, such as the number of PVRs, storage capacity, and the portion of

storage assigned for time shifting.

61

3.5.1 Effectiveness of Proposed Schemes in a Single Loop

The default values of these parameters are shown in Table 3-3. They are used

throughout our simulations unless otherwise indicated. It is assumed that each TV

program has HD quality of 19.4Mbps playback rate. The degree of access skewness

among TV programs follows the Zipf distribution with α = 0.271, which is typically used

for video rental distribution [17, 79].

Table 3-3. Simulation parameters

CATEGORY PARAMETER VALUE

FC-AL

data transfer rate 400 MB/s

propagation delay 3.5 ns/meter

per node delay 240 ns

Disk

capacity 500 GB

cache 32 MB

data transfer rate 29 ∼ 65 MB/s

seek time 4.7 ms

rotational latency 3.0 ms

Storage saving scheme

probability of each PVR being alive 70%

threshold 0.9

storage portion for time-shifting 30%

Zipf distribution (α value) 0.271

Multiple-loopshared disk interface configuration dual-loop interface

number of network disks 10% of all devices

Our proposed storage-saving schemes are first evaluated in the single-loop

architecture with various parameter settings such as the ratio of PVRs and network

disks, Palive values and threshold values. Fig. 3-3A shows the improved storage hours

according to the different number of network disks deployed in terms of percentage with

our proposed schemes. We can see that the improvement reaches almost 133% better

with seven network disks, (i.e., 119 PVRs). The improvements tend to decrease with

62

an increase in the number of network disks. This is because the number of PVRs is

decreasing when adding more network disks. Thus, we can confirm that our schemes

work better when more PVRs are deployed (i.e., the trend). In addition, Fig. 3-3B

shows the effect of the ratio of PVRs to network disks. When the ratio is increasing, the

improvement tends to get better up to 16%, which implies that the improvement cannot

reach higher without adding many network disks.

Lastly, Fig. 3-4A demonstrates how the various Palive can affect the system

performance. As we expected, the larger the value of Palive , the better system performance.

That is, the performance is improved when a large value of Palive is set by the users. This

means that when the probability that each PVR can be alive is high, the system shows

better performance. Fig. 3-4B illustrates the effects of various threshold values. As the

value gets larger, the system performance is decreased. This is because the system

needs more PVRs in order to meet the given threshold values. Thus, the larger thresh-

old shows the smaller number of storage hours in terms of the system performance.

3.5.2 Effectiveness of Proposed Schemes in Multiple Loops

In addition, we employ dual-loop interfaced shared disks between two distinct loops

in order to organize the multiple loop architectures. We configure multiple loops so that

each loop can reach any other loop with only one hop. This means that any PVR can

access any other PVR or network disks in the other loops via only one shared disk.

Table 3-4 shows how many PVRs and network disks there are in a community

network depending on the number of loops. Though we are still searching for the optimal

ratio, we have chosen to assign 10% of all possible attached devices to network disks in

this chapter. It can be seen that, when employing ten loops, our architecture can support

more than 1100 devices in a community network. The simulation results demonstrate

that our proposed architecture significantly increases program storage hours by reducing

the duplicated storage of popular programs in a community network. The following

63

A Improved storage hours

B Effects of PVR to NDisk ratio

Figure 3-3. Effects of PVRs and network disks

64

A Effects of Palive

B Effects of threshold

Figure 3-4. Effects of Palive and threshold

65

Table 3-4. The ratio of pvrs and network disks in a community network

NUMBER OF LOOPS NUMBER OF PVRS NUMBER OF NETWORK DISKS

1 115 12

2 228 25

3 341 37

4 452 50

5 563 62

6 673 74

7 782 86

8 890 98

9 997 110

10 1103 122

subsections describe the achieved efficiency of the system performance based on

various system aspects.

Figure 3-5. Effectiveness of our proposed architecture

66

3.5.3 Effectiveness of Our Proposed Architecture

While varying the number of PVRs, we compare program storage hours, which

is the main performance metric in our architecture. The direct comparison is between

our replacement scheme when on (called scheme on case) and off (called scheme

off case). With scheme off case as the baseline system, the system stores at least

one copy of each program on network disks irrespective of the number of copies of the

program on PVRs, so that PVRs can always access the programs even if they are not

stored on their local hard disks.

Fig. 3-5 shows that scheme on case outperforms scheme off case by an average

of 69.7%. This is because the scheme on case can reduce the duplicated storage of

programs effectively, considering the characteristics of TV recording pattern, i.e., access

skewness to popular TV programs. As the number of PVRs increases, the number of

copies of each program also increases. This implies that, as more PVRs are involved,

our scheme can be further improved by reducing the number of redundant copies

of each program. This also enables network disks to save storage space that would

otherwise store excessive copies of popular programs. Note that the number of PVRs in

Fig. 3-5 is proportional to the number of loops, as listed in Table 3-4.

3.5.4 Impact of PVRs’ Storage Portion for Time-Shifting

We then examine the impact of PVRs’ storage portion assigned for time-shifting

on the performance. As expected, Fig. 3-6 shows that, as a higher storage portion is

assigned for time shifting, performance improvement also increases. For example, let

us consider the case where we employ 900 PVRs in a community network. Compared

to when only 10% of PVRs’ storage is reserved for time-shifting, the program storage

hours become 17.1%, 32.9%, 44.7%, and 56.1% longer when 20%, 30%, 40%, and

50% are reserved, respectively. There are likely to be more duplicated programs as we

have more storage space for time-shifting.

67

Figure 3-6. Impact of PVRs’ storage portion for time-shifting

It is also interesting that the degree of improvement percentage decreases as

the portions increase. For example, when employing 900 PVRs, extended hours

between each of two consecutive portions from 10 to 50% are 24, 21, 18, and 16 hours,

respectively. Thus, it can be seen that, as more programs are accommodated, our

architecture continues to improve the performance but the improvement percentage

decreases.

3.5.5 Impact of Storage Capacity

In this section, we investigate the impact of disk storage capacity of each PVR

on storage hours. We employed three different disk capacities of 100GB, 300GB, and

500GB for scheme on and off cases, respectively. Fig. 3-7 shows that scheme on case

on average achieved the performance improvement by 56.3%, 65.3%, and 69.7% for

100GB, 300GB, and 500GB, respectively, compared to the scheme off case. That is, as

the storage capacity of each PVR increases, the program storage hours get longer. This

is because PVRs’ storage portion for time shifting increases with the increased storage

68

Figure 3-7. Impact of storage capacity

capacity as their storage capacity is larger. It can be also seen that the performance

at 300GB in the scheme on case can work similarly as 500GB in the scheme off case.

This implies that our proposed architecture can achieve acceptable performance even

with relatively little storage capacity.

3.6 Summary

Community networks can serve to share HD-quality contents among the community

members. To provide an infrastructure of community networks, we propose the

multiple-loop FC-AL-based community network architecture with shared disks, which

can overcome the limitations of single-loop-based architecture without expensive FC-AL

switches.

Based on the multiple-loop-enabled community network infrastructures, we have

proposed storage saving schemes for both PVRs and network disks. The goal of

storage saving schemes is to reduce the number of duplicated copies of popular

programs to extend program storage hours of TV programs. The extensive simulations

69

show that our proposed architecture can extend the program storage hours significantly.

Since communities will require more storage capacity in the future with increased

content quality, our proposed architecture is expected to be widely applied to large-scale

community networks.

70

CHAPTER 4AN MST-BASED NETWORK ARCHITECTURE FOR SHARING BROADCAST TV

PROGRAMS

4.1 Motivation

The suggested multiple-loop architectures using the shared disks in the previous

chapters are based on the complete graph (CG) topology for less total traffic. We

determined how the complete graph topology could minimize the total traffic and would

be suitable for the multiple-loop architectures with shared disks. Nevertheless, it has

a critical limitation in terms of topology organization. In other words, the CG topology

requires many connect-disks, i.e., shared disks, just for the topology connectivity.

Namely, if there are n loops in the architecture, the system demands at least nC2 shared

disks merely for the system organization, thereby making it less likely to be able to

construct a real network architecture, especially if the number of loops is increasing.

Additionally, in multiple-loop organization, the initial shape of a loop should be

changed in order to place a shared disk between loops, which results in an increase

in the loop size as shared disks are added. For example, in order to connect two loops

that are physically somewhat apart, it is clear that the insertion of the shared disk

requires the modification of those two loops. As a result, the size of each loop increases

when a shared disk is located between any two loops. It is well known that, to provide

better performance, the size of an FC-AL loop is significant in order to be suitable

for high-quality content delivery in realtime [60]. This indicates that the average loop

size should be larger when each loop has more neighbor loops connected by shared

disks and would deliver worse performance due to increased propagation delays and

arbitration times.

Therefore, in this chapter, we first introduce a novel multiple-loop architecture using

a minimum spanning tree (MST) based topology with less connectivity and less average

loop size. In fact, the CG topology is an extreme case, having the most number of

connectivities by 1-hop reachability and thus the least total traffic with distributed traffic

71

load, while the MST-based topology is an example of another extreme case, having the

least number of connectivities and thus concentrating traffic load on the shared disks

that are selected for the MST topology.

We then describe how we can organize the MST-based topology, so that average

loop size in the topology can be minimized and every loop can reach any other loops

within a 2-hop or 3-hop, which affects the topology-caused total traffic since the loaded

traffic is additive when more hops are involved in routing. Nonetheless, once the

topology is determined, the MST-based topology only permits a fixed routing path, which

still causes a traffic load concentration on the fixed routing path.

Consequently, we propose an innovative MST-based graph topology, named MSG

topology, which is based on the 2-hop and 3-hop MST topology, but allows multiple

paths and graph organizations, using enabling traffic load distribution via a less-loaded

path among those possible paths. We describe how the MSG topology is achieved and

compare the MSG topology with the MST topology and the CG topology. Our simulation

results, then, reveal that the MSG topology multiple-loop architecture can provide a very

close average loop size compared to MST topology cases with remaining constant and

close enough in terms of total traffic and average reject ratio compared to the CG cases

even with much less topology connectivity.

The remainder of this chapter is organized as follows: Section 4.2 describes

previous work related to this chapter. Section 4.3 explains the overall architecture for

the FC-AL multiple-loop architecture with shared disks. Section 4.4.1 specifies the

motivation by which the MSG-based topology is devised. Section 4.5 presents the

detailed algorithms on how to organize 2-hop/3-hop MSTs and MSGs, respectively.

Section 4.6 illustrates extensive simulation results with CGs, MSTs, and MSGs. Finally,

Section 4.7 offers conclusions.

72

4.2 Related Work

The functionalities and the effects of PVRs have been introduced in [45, 52].

Moreover, the capabilities of TV content-sharing between PVRs have been examined in

[35, 49].

Meanwhile, the fiber channel has been presented as one of standards to configure

the storage area networks [34] and the performance and characteristics of FC-AL

have been analyzed in [19, 37, 60]. Additionally, S. Chen et al. have investigated the

multimedia server organization based on FC-AL by several advantages such as a direct

attachment to the storages and the desirable performance at a low cost [12, 13].

Integrating all those components including PVRs and the FC-AL, the feasibility of

a multimedia content-sharing architecture has been proposed in [14, 38]. However, E.

Kim et al. have not considered the scalability issues to construct the FC-AL-based PVR

network architecture [38] and the complete graph topology has been mainly discussed in

designing a scalable multiple-loop architecture [14].

4.3 TV Content-Sharing Architecture

In this section, we introduce a novel TV content distribution architecture that

supports sufficient storage bandwidth, massive storage capacity, and each user’s

capability/willingness to contribute their resources to the network. We first explain

the FC-AL single-loop architecture, then we describe the suggested multiple-loop

architecture using shared disks as the PVR network infrastructure [14].

4.3.1 Multiple Loop Architecture

We have developed a scalable multiple-loop network architecture that has no

limitation on the number of attached devices as long as the FC-AL loops can be

technically configured in the multiple-loop architecture [14]. When extending the

single-loop system to the multiple-loop system, however, we need to devise a bridging

mechanism to relay the requested program between loops. We thus adopt shared disks

that connect two distinct loops and relay requested programs from one loop to the other.

73

TV Broadcasting Server

Service Provider

Service Provider

Small PVR Network

Large PVR Network

SDisk

Internet

PVR Home

A TV broadcasting to PVR networks

B The structure of an FC-AL single loop C Sample three-loop architecture with shared disks

Figure 4-1. Overall TV content distribution architecture

74

Since the FC standard allows multiple interface devices for such reasons as durability

[34], we can implement the shared disks without modifying FC protocols.

Shared disks are essential components in the multiple-loop architecture because

they are responsible for relaying programs between loops. We adopt dual-interfaced

hard disks as shared disks and connect each interface to different loops in order to

organize the multiple-loop architecture. Note that the number of shared disks between

a specific pair of loops depends on estimated network traffic. For instance, although the

number of shared disks between any loop in Fig. 4-1C is one, it can be greater than one

if ongoing traffic is more than can be covered by a single shared disk as shown in Fig.

4-2.

To make it feasible to relay programs between a pair of system components without

involving servers, we adopt FC disks that provide a direct data transfer function among

them such as extended copy (X-copy or E-copy) and disk-to-disk (D2D) transfer. With

this function, a server only designates involved disks for their roles, i.e., one for source

and the other for destination. In fact, this function has already been developed by

several companies for serverless data backup technologies using FC disks with limited

processing capabilities [8]. In our architecture, each loop has a DS that maintains a list

of TV programs stored in all components. The DS is responsible for triggering X-copy

module embedded in each shared disk as an initiator when performing the direct data

transfer. This function can reduce a data-relaying latency and avoid the situation where

servers become a performance bottleneck when they must relay all the data among

loops through their main memory.

The program relaying process in our system is performed in two phases: location

and routing determination using FC-AL protocol and actual block read/write between

two devices using FC protocol. In the first phase, the PVR first asks a DS where the

requested program is located and then the DS informs the PVR of the entire routing path

75

DS0 DS1 DS2

Sdisk(b) Sdisk(c)

NdisksNdisks

PVR(a) PVR(d)

Ndisks

SdisksSdisks

PVRs PVRs PVRs

Loop 0

Loop 1

Loop 2

Figure 4-2. How to relay a TV program using shared disks in a triple-loop

from target PVR from which it the PVR can receive the program. In the second phase,

the actual data block transfer from the target PVR via shared disks is performed.

Fig. 4-2 illustrates how a program can be transferred from a requesting PVR to a

target PVR via shared disks in a triple loop using FC-AL and FC protocols. Basically,

each DS provides necessary information to a requesting PVR and also works as an

initiator to trigger the X-copy function between any pair of two devices. In the first phase,

when PVR(a) has a request for a TV program, it asks DS0 which device it can be served

from (¬). The DS0 then finds out from its maintained information that it is located in

PVR(d) and determines a routing path, including proper loops, i.e., loop-1 (­) and loop-2

(®) in this scenario. Once the routing path including multiple loops is determined (¯),

the DS0 informs PVR(a) of a relaying device, Sdisk(b). By repeating this procedure

until reaching the target PVR, PVR(a) obtains a connection path to PVR(d) through

shared disks, i.e., Sdisk(b) and Sdisk(c). In order to establish an actual connection to the

target PVR, PVR(a) sends ARB(a) to loop-0 for an arbitration and then sends OPN(b)

to Sdisk(b) to open a connection to Sdisk(b) after winning the arbitration. Sdisk(b) then

sends R RDY back to PVR(a) to settle a communication with PVR(a) (°). At the same

time, DS1 issues a direct data transfer command to Sdisk(b) so that Sdisk(b) should

76

setup a communication with Sdisk(c) in the loop-1. Similar to °, ARB(b), OPN(c) and

R RDY is also sent in the loop-1 for the communication between Sdisk(b) and Sdisk(c)

(±). In the loop-2, DS2 coordinates the communication between Sdisk(c) and PVR(d) as

an initiator with the same commands as ones used in previous loops (²).

After all routing connections are established from PVR(a) to PVR(d) via Sdisk(b)

and Sdisk(c), in the second phase, data blocks are transmitted along with the determined

path. DS2 issues an X-copy command to PVR(d) so that PVR(d) should write the

requested program onto Sdisk(c). PVR(d) thus sends FCP CMND IU to Sdisk(c) as

a write operation. Sdisk(c) then sends FCP XFER RDY IU to PVR(d) to receive data

blocks and PVR(d) sends FCP DATA IU back to Sdisk(c) to transmit an additional

data block. This pair of FCP XFER RDY IU and FCP DATA IU between Sdisk(c) and

PVR(d) continues to be transferred within a specific period of time, i.e., cycle time,

until all the block-writing to Sdisk(c) is finished (³). In fact, each DS coordinates the

block transmissions so that each relaying shared disk can continuously transfer the

incoming data blocks to the next device with only storing them into its buffer temporarily,

not storing them onto its hard disk. Similar to ³, the data block transmission between

Sdisk(c) and Sdisk(b) (´) and between Sdisk(b) and PVR(a) (µ) is performed with

the same commands such as FCP CMND IU, FCP XFER RDY IU, and FCP DATA IU,

by the coordination of each loop’s DS, i.e., DS1 and DS0, respectively. Note that, if a

requesting PVR can receive the requested program from a target PVR in the same loop,

other DSs and shared disks do not need to be involved.

Based on the program relaying mechanism via shared disks, Fig. 4-1C illustrates

the fundamental architecture configuration with three loops and shared disks. Specifically,

every loop is directly connected by all the other loops via a shared disk per loop.

For example, loop-0 is directly linked to loop-1 and loop-2 by each shared disk. This

organization allows every loop to reach all the other loops with only one hop, i.e., one

77

shared disk. In other words, the multiple-loop architecture organized by the CG topology

connects all loops by providing 1-hop reachability between any two loops.

Thus, we can make one important observation: the CG topology requires at least

as many shared disks as the directly connected loop number only for the purpose of

maintaining topology connectivity. We therefore analyze this problematic topology

connectivity issue and propose a novel multiple-loop topology in Section 4.4, in order to

organize an efficient multiple-loop architecture.

4.4 Enhanced Multiple-loop Network Architecture

4.4.1 Overview

The CG topology multiple-loop architecture is likely to be impractical when we

organize a real network infrastructure since every loop needs to be attached by all

the other loops for 1-hop connectivity. Additionally, this would be extremely expensive

because all loops are connected by a physical fiber channel without considering how

far they are actually apart. Therefore, we need to develop a tree-based multiple-loop

architecture for high-quality content sharing among PVR users by facilitating all the

pairwise reachabilities.

When placing a shared disk between two loops, we can regard the modified shape

of each loop, as in Fig. 4-3A, without loss of generality, while maintaining each loop

structure in order to organize the multiple-loop architecture. Specifically, if two loops

are d kilometers apart, we then put a shared disk in the middle of the distance, i.e.,

d/2 kilometers, by attaching each loop to its shared disk. Fig. 4-3A illustrates how, as

a basic case, each loop can be modified when a shared disk is inserted between two

different loops. The key observation here is that each loop size should increase in order

to reach a contact point, i.e., the shared disk between two loops. In addition, Fig. 4-3B

shows how we organize a CG topology with four loops, demonstrating how each shared

disk can be directly connected in order to maintain each loop structure. In fact, we can

link the inserted shared disk to any device on the loop as long as the extended loop size

78

A 2-loop generic for multiple loops B 4-loop CG

C 4-loop MST D 4-loop MSG

Figure 4-3. Examples of CG, MST, and MSG architectures

can be reduced and the loop structure can be maintained. Nevertheless, the loop size

tends to increase whenever a shared disk is located between two different loops. This

means that the CG topology multiple-loop architecture would have the largest loop size

compared to any other topology architectures since it has the most connected loops, as

many as (n − 1) neighbors, where n is the number of deployed loops.

79

It is well known that the performance of the FC-AL decreases when the loop

size increases [60], although it is believed that the FC-AL can cover up to 10 km.

Furthermore, practical issues arise when a multiple-loop architecture is really applied as

a network infrastructure in real time. That is, it is also an important factor in how many

loops are necessary or how long the physical fiber channel has to be in terms of the

actual cost and the effectiveness of the system.

Moreover, when a shared disk is inserted in the middle of two loops, we can further

reduce the modified loop size if we choose the desirable, i.e., closest, loops where a

shared disk would be placed. When this approach is applied, the possible 4-loop MST

architecture is shown in Fig. 4-3C. Nonetheless, the MST architecture only allows one

fixed routing path between any pair of loops, thereby causing load-concentration on that

path.

Consequently, we introduce an innovative MSG topology multiple-loop architecture

in this chapter that provides acceptable performance in terms of average loop size

and average reject ratio while supporting multiple-path routing within 2-hop or 3-hop

reachability. An example of 4-loop MSG topology is shown in Fig. 4-3D, implying there

can be multiple paths between two loops for load distribution. For instance, loop-0 can

reach loop-2 via sdisk-a, loop-1, and sdisk-b or via sdisk-d, loop-3, and sdisk-c in Fig.

4-3D. We describe the MSG topology in greater detail in the next section and examine

its performance by various simulations in Section 4.6.

4.5 Problem Definition and Formulation

4.5.1 Problem Statement

As described in Section 4.4.1, the previous CG approach proposed in [14] has

not taken into account practical aspects of the problem, i.e., loop size, and has been

chosen as the best selection by comparing several possible network topologies. The

only factor considered in [14] is the total network traffic traversing network topologies

80

given same amount of data requests in a sense that the more total network traffic is, the

more shared disks are needed.

However, the total network traffic, which is proportional to the number of shared

disks, is not the only design factor we have to consider when designing network

topologies for high-quality content-sharing in a PVR-attached network. The principal

design factors that should be considered are: Network throughput, Hop number

between pairs of loops, Accept/reject ratio of requests, Number of users that can be

accommodated, Number of shared disks, Loop capacity, and Loop size. Network

throughput is a well-known performance metric to evaluate a certain network situation.

Hop number between pairs of loops should be bounded so we can guarantee that

a requested program can be accessible within some delay and without quality

degradation. The hardware attributes of FC shows that the quality of communication

between two devices in a FC gets poor drastically as the distance is getting farther.

Accept/reject ratio of requests and the number of accommodable users are major design

criteria before deploying network topologies. Loop size and the number of shared disks

reflect budget issues as well as feasibility of topologies. Loop capacity is one of inherent

limitations of FC loops according to FC capacities.

However, it is not true that each of these design factors is orthogonal to the other

factors. In general, network throughput can be approximated by total network trafficaverage hop number.

Thus, network throughput and hop number between pairs of loops are closely correlated

since network throughput decreases as average hop number of paths on that request

should traverse. Number of shared disks is also dependent on network throughput,

which means poor network throughput leads to a greater number of shared disks to

handle increased network traffic. When looking at the number of shared disks and

loop size, they also have on inversely proportional relationship. As shown in Fig. 4-1,

the loop size has an increasing tendency as the number of shared disks between

loops increases. In this chapter, we look for a solution in which loop size is minimized

81

while the accept/reject ratio falls into reasonable range. Due to the fact that loop size

is proportional to the distance between loops as described in Section 4.4.1, we can

translate the problem of minimizing loop size into the problem of minimizing total

inter-distance of loops connected by shared disks. The complications that the problem

has as objectives or as constraints deter us from deploying simple combinatorial

algorithms such as Kruskal’s Minimal Spanning Tree (MST) algorithm and so on.

Therefore, we formulate this design problem as an optimization problem with one of

two possible objective functions and several constraints. Our mathematical formulation

is motivated by the Diameter-constrained Minimum Spanning Tree Problem (DMST)

based on network flow models [25], which is mixed-integer linear programming (MILP).

Once a problem is formulated in a linear programming model, the linear programming

model allows us more flexibility compared to other simple combinatorial algorithms. In

the following subsections, we describe optimization criteria and associated constraining

conditions to solve our problem.

minimize∑

e∈E

cexe (4–1)

subject to∑

e∈E

xe = n − 1 (4–2)

∑

j∈V

y pqij −

∑

j∈V

y pqji =

1, i = p0, i 6= p, q, ∀i , p, q ∈ V−1, i = q

(4–3)

y pqij + y pq

ji ≤ xe , ∀(i , j) ∈ E ,∀p, q ∈ V (4–4)∑

(i ,j)∈E

(y pqij + y pq

ji ) ≤ D, ∀p, q ∈ V (4–5)

y pqij ∈ {0, 1}, ∀(i , j) ∈ E ,∀p, q ∈ V (4–6)

xe ∈ {0, 1}, ∀e ∈ E (4–7)where ce : Edge distance,

D : Maximum hop number

Figure 4-4. MST formulations

82

4.5.2 Minimum Spanning Tree

The DMST based on network flow models [25] imposes a bound on the diameter of

a tree, which is the maximum number of edges, i.e., the maximum hop number, in any

paths between any pairs of its nodes. When D ≥ 4 where a diameter is denoted by D,

the DMST problem is proved to be NP-Hard [25] whereas when D = 2 or 3, the problem

is solvable in polynomial time.

The DMST based on network flow models fits well into our problem since the

hop number for any path is constrained by a given constant, so subsequently we can

bound the total network traffic. As for the diameter constraint, we do not need D ≥ 4

because in that case, obtaining a target program from peers beyond 3 hops is highly

likely to be more costly than getting one from a local server from the perspective of

delay and network resource consumption. Thus, as a first try, we directly applied the

DMST based on network flow models with diameter 2 or 3 to our optimization problem.

In terms of time complexity, it does not take very long to solve the MILP formulation

when the number of loops does not exceed around 20 based on an assumption that an

adopted network has roughly 1,000 ∼ 2,000 subscribers. The notations and equations

are borrowed from [28] whenever possible.

The objective of this MILP formulation is to minimize total distance between loops

connected by shared disks in Eq. 4–1 when every node sends a unit amount of flow,

1, to all other nodes. The decision variable xe represents whether a certain edge is

included in a tree or not. Another decision variable y pqij denotes the amount of flow on

edge (i , j) between nodes p and q. The constant ce is an edge distance, determined

once the locations of loops are known in advance. The resulting graph is constrained

to be a tree by Eq. 4–2. The constraints 4–3 and 4–4 come from those of well-known

multi-commodity network flow problem, making every flow obey flow conservation and

link capacity limit rule. The Eq. 4–6 limits the hop number of all the paths by D, which a

node should traverse to reach the other nodes.

83

minimize∑

e∈E

cexe or∑

(i ,j)∈E

∑

p,q∈V

y pqij (4–8)

subject to

∑

j∈V

y pqij −

∑

j∈V

y pqji =

1, i = p0, i 6= p, q, ∀i , p, q ∈ V−1, i = q

(4–9)

(n − 1) ≤∑

e∈E

xe ≤ 2(n − 1) (4–10)∑

j∈V

y pqji ≤ L(n − 1), ∀i , p, q ∈ V (4–11)

y pqij + y pq

ji ≤ xe , ∀(i , j) ∈ E ,∀p, q ∈ V (4–12)∑

(i ,j)∈E

(y pqij + y pq

ji ) ≤ D, ∀p, q ∈ V (4–13)

y pqij ∈ {0, 1}, ∀(i , j) ∈ E ,∀p, q ∈ V (4–14)

xe ∈ {0, 1}, ∀e ∈ E (4–15)where ce : Edge distance,

D : Maximum hop number ,L : Maximum load rate per node

Figure 4-5. MSG formulations

4.5.3 Minimum Spanning Tree-based Graph

We have observed that the resulting DMSTs with diameter 2 or 3 hops have a

tendency that a few loops (nodes) are severely congested, i.e., not load balanced. That

also leads to a worse accept/reject ratio of requests. That is because among the design

factors described in Section 4.5.1, loop capacity is not reflected into constraints of the

MST formulation.

To overcome these drawbacks of the MST approach, we have adjusted previous

constraints such that traffic going through a loop is bounded and the resulting graph may

not necessarily be a tree.

As objective functions, we can take one of two possible functions as below

depending on which design factor has a priority. If the loop size is the top priority, setting

minimizing total distance to the objective function is reasonable. On the other hand, if

84

we more focus on the quality of network, the objective function should be minimizing

total network traffic because minimizing total network traffic has the same meaning as

enhancing accept/reject ratio of requests and the number of accommodable users.

Formally, the objective functions can be set as in Eq. 4–8. The constraint enforcing

the resultant graph to be a tree in the previous MILP formulation is removed in this

formulation. Instead, the constraint is loosened by setting the upper limit of the number

of edges to 2 × (n − 1) as in Eq. 4–10. Moreover, the load balancing constraint in

Eq. 4–11 is added.

The inequality of Eq. 4–11 can be understood intuitively as bounding the total

amount of flows in which a node is involved by L × (n − 1). If a certain node is not used

as a intermediate node where flows between other nodes traverse, the total amount of

flows in which a node is involved is (n − 1). If we set L to 2, it can be translated that one

node does not sacrifice its bandwidth for other nodes, more than for itself since here one

node represent a loop. It is a reasonable constraint that no participant does not want

to give its resource more than it needs. Hence, in this chapter, we conduct following

experiments when L is given as 2. The other constraints, Eq. 4–12 through Eq. 4–15,

are same as constraints in the MST formulation.

The reason load balancing among loops is possible is that our formulation is

based on multi-commodity network flow models. The DMST based on multi-commodity

network flow models assumes that each node generates the same amount of traffic to

the other nodes; this can be regarded as the most congested network status. For this

reason, the load constraints on each node can easily reflect the network situation where

requests are rejected due to a shortage of loop capacity.

In Section 4.6, we will see load balancing as an objective function results in the

best topology in terms of both loop size and reject ratio. Alternatively, to achieve

both objectives, minimizing loop size and load balancing, we can take either of

two approaches. One is to combine two objective functions additively with scaling

85

Given the number of loops ,

find the lower and upper bound

of load on each loop .

Compute the median value of

lbound and ubound.

Solve MILP with an objective of

minimizing total distance and the

load constraint of the median

value.

Feasible

solution found?

lbound = ubound ?

Set lbound to the

median value .

Set ubound to

the median value .

No

Yes

Exit:

Optimal solution

found.

YesNo

Set lbound and ubound to the

calculated lower and upper bound

values respectively .

Figure 4-6. Flowchart for load-balanced MSG

factors, which is called multivariate objective function. The other approach is to use

one objective function while searching the all solution space of the other objective

function. Suppose that we need a solution that satisfies the limit of loop size while

maximizing load balancing. We can get the solution by performing a binary search on L,

the maximum traffic limit variable, to minimize L as long as the solution is feasible, i.e.,

every loop size is less than or equal to the limit of loop size. The feasibility is verified

86

by computing the loop size of the solution, which is obtained by setting the objective

function and L to minimizing total distance and a binary search value, respectively, and

comparing the loop size with the loop size requirement. Note that a smaller L means

a more load balanced solution and too small a value of L may lead to an unfeasible

solution. This algorithm is shown as a flowchart in Fig. 4-6. In this algorithm, the

lower bound (lbound) of L is set to 1, whereas the upper bound (ubound) of L is set to

n × (n − 1) because each loop sends flow 1 to every other loops in DMST network flow

model, thus, total traffic in DMST network flow model cannot exceed n × (n − 1). It then

iterates a binary search until it reaches the minimum value of L. Therefore, the solution

is feasible as well as it is optimized for the most load balancing graph topology. The

step-by-step operations of the algorithm in Fig. 4-6 are given below:

• STEP 1: Set lbound and ubound to 1 and n, respectively.

• STEP 2: Check on the exit condition that lbound equals ubound. If true, exit withthe optimal solution.

• STEP 3: Try MILP with the median value of lbound and ubound.

• STEP 4: If a feasible solution is found, it means the optimal solution is in betweenlbound and the median value. If not found, it means the optimal solution is inbetween the median value and ubound.

• STEP 5: Update lbound or ubound, and jump back to STEP 2.

4.6 Performance Evaluations

In this section, through extensive simulations, we examine various types of

topology-based multiple-loop architectures, such as average loop size in order to

validate their performance total loaded traffic, and average reject ratio, while varying the

number of deployed loops.

In the simulations, when TV programs are broadcast, the programs are distributed

evenly among loops and the probability that each program is requested is uniform so

that the amount of data traffic between each pair of loops can be fairly compared. The

request rate of program playback follows a Poisson distribution and each PVR issues

87

Table 4-1. Simulation parameters

CATEGORY PARAMETER VALUE

FC-AL

data transfer rate 1 Gb/s

propagation delay 3.5 ns/meter

per node delay 240 ns

Disk

capacity 300 GB

cache 32 MB

data transfer rate 29 ∼ 65 MB/s

seek time 4.7 ms

rotational latency 3.0 ms

TV programs

HD-quality 19.4 Mbps

SD-quality 5 Mbps

length 60 minutes

requests for different programs every 60 minutes. It is also assumed that half of attached

devices are PVRs, and the rest are for network disks and shared disks in each loop. The

details of parameters used for disks, FC-AL, and TV programs are illustrated in Table

4-1.

Fig. 4-7 illustrates that average connection setup time in a double-loop architecture

as a basic case of multiple-loops, based on the description in Section 4.3.1. We have

evaluated the average setup time based on the double-loop configuration while adding

more PVRs, i.e., from 10 up to 70, in each loop. As shown in Fig. 4-7, the setup time

tends to increase with more PVRs. Nevertheless, we can see that this connection

setup time is reasonable since our system performs less than 16 msec with high-quality

multimedia data in the 70-PVR case, whereas [64] shows its setup time as around 6.2

msec using the FC-AL switch device with only 50% of possible generated traffic.

We have also evaluated the worst case of startup latency when three loops are

involved for relaying a program. The latency of our system is mainly caused by the first

phase of the relaying process since the startup latency is greatest when streaming

88

0 10 20 30 40 50 60 70 800

4

8

12

16

20

number of PVRs in a loop

aver

age

sert

up

tim

e (m

sec)

Figure 4-7. Impact on average setup time in a double-loop

a program between two PVRs. We have observed with parameter values in Table

4-1 that startup latency is around 29.17 msec in the worst case when all the devices

are participating in every loop arbitration, involved devices have lowest priorities, and

required data blocks must be read from disks. This is a reasonable delay considering

one cycle time is usually over 10 times longer than this delay.

Moreover, in order to evaluate average loop size, in a 30 km × 30 km area, we

randomly distribute a given number of loops as long as the CG configuration is achieved.

Once the CG topology is organized with the given number of loops, we then configure

both MST and MSG topologies from the deployed loops for the CG. We also validate the

proposed MSG algorithm described in Section 4.5.3 in terms of the effectiveness of the

the load-balancing among loops and shared disks.

4.6.1 Average Loop Size

Fig. 4-8 illustrates that, when loops are added, average loop size in the CG case

increases linearly, while the MST and MSG topology cases stay constant. Essentially,

the loop size is closely related to the average distance between two loops since the

89

5 6 7 8 9 10 11 12 13 14 15

4

5

6

7

8

9

number of loops

aver

age

loop

siz

e (k

m)

CGMST−2HMST−3HMSG−2HMSG−3H

Figure 4-8. Average loop size

shared disk is placed in the middle between those two loops. In other words, if the

distance between two loops becomes larger, the modified loop size with the shared

disk would become quite sizable. In addition, a large FC-AL is less desirable due to

performance degradation from the rapid increase of propagation delay [60]. Thus, we

can see that the MST topology performs best and the CG topology performs worst in

terms of average loop size. This is because CG requires every loop to be connected to

all other loops, whereas the MST topology only needs one or two connected loops via

shared disks, and the MSG topology shows 2 × (n − 1) connectivity on average by the

algorithm in Fig. 4-6.

4.6.2 Total Traffic and Average Reject Ratio

We also explore total traffic using different topologies supporting the whole

architecture, which is mainly affected by the routing, i.e., how best to reach the target

loop from the request loop. As shown in Fig. 4-9A, it is clear that the CG topology

presents the least total traffic in a given number of loops since it guarantees only 1-hop

routing between any pair of loops, requiring at least nC2 connect-disks for the 1-hop

90

5 6 7 8 9 10 11 12 13 14 150

100

200

300

400

500

600

700

800

900

1000

1100

number of loops

tota

l tra

ffic

(num

ber

of p

rogr

ams)

CGMST−2HMST−3HMSG−2HMSG−3H

A Total traffic

5 6 7 8 9 10 11 12 13 14 15

0

5

10

15

20

25

30

35

number of loops

aver

age

reje

ct r

atio

(%

)

CGMST−2HMST−3HMSG−2HMSG−3H

B Average reject ratio

Figure 4-9. Total traffic and average reject ratio

91

reachability, where n is the number of loops. Although the MST cases show the highest

total traffic, this requires the least number of connect-disks because of the increased

routing hops and the fixed routing path between the request loop and the target loop.

In Fig. 4-9B, MST shows the worst average reject ratio compared to CG and

MSG. CG has the best reject ratio, even less than 0.5%, since it guarantees the best

load distribution with its own dedicated routing path to all loops. Nevertheless, without

using the required hard-connected routing paths to every loop, MSG can perform with

less than a 5% reject ratio even in the worst case with fewer connect-disks than CG.

Surprisingly, due to more flexible routing paths than MST, the MSG also maintains a

constant reject ratio even when more loops are deployed.

Consequently, the MSG reveals the superiority reflected by the extreme CG and

MST topologies, in terms of average loop size, total traffic, and average reject ratio.

Since the MSG is derived from the MST, it also has a constant average loop size.

Moreover, since MSG further allows the graph characteristic, it can provide more

flexible routing paths supporting load distributions, which can affect the amount of total

traffic and the average reject ratio. Therefore, we can conclude that the MSG-based

multiple-loop architecture can distribute high-quality content-sharing effectively among

PVR users, delivering outstanding performance to the whole system at lower total cost.

4.7 Summary

As an economical and practical solution for multiple-loop architecture, the proposed

MSG topology demonstrates a constant average loop size, even as the number of

loops grows. This is similar to the MST case, without a loss of FC-AL performance from

the requirement for a long fiber channel. Furthermore, the MSG topology generated

a reasonable reject ratio, less than 5%, even in the worst case, while the reject ratio,

remarkably, is not affected by the number of attached loops, because MSG can

distribute the loaded traffic efficiently by choosing a less-loaded path among the

multiple candidate routing paths. The MSG thus showed an acceptable total traffic,

92

which is sufficiently close to the CG case, although much less than the MST case. Our

proposed MSG-based multiple-loop architecture is therefore expected to be a significant

and practical architecture solution capable of supporting many more PVRs with an

increased number of loops, thus allowing high-quality realtime content sharing and

efficient distribution.

93

CHAPTER 5CONCLUSIONS AND FUTURE WORK

This dissertation made some contributions for organizing a multimedia-enabled

network architecture where high-quality P2P content sharing is supported. In order to

configure an effective network architecture, we have introduced the FC-AL technology

as a high-speed and broadband network connection and the PVR as a multimedia

home device. Moreover, we have presented the shared disk in order to extend the

multiple-loop architecture for a scalable network architecture design.

In the first part of this dissertation, we have proposed a scalable multiple-loop

architecture using shared disks, which supports HD-quality content distribution efficiently

and effectively. In our proposed architectures, we have showed that the topology

design affects the total loaded traffic amount which influences the scalability in terms

of the total attachable device number, thereby giving us a way to determine which

architecture design is more desirable. In addition, we have realized that we have to

reduce the number of shared disks in order to obtain a better scalability which is also a

key factor to design the multiple-loop architectures. Basically, we have tried to reduce

the topology-caused total traffic by adopting the Complete Graph (CG) topology design

in order to use the fewest number of shared disks, which is the initial approach to

determine the desirable multiple-loop architecture.

In the second part, we have devised the storage saving scheme which tries to utilize

the whole system storage including both network disks and PVR storage. The storage

saving scheme operates based on the given threshold value which determine whether

or not a new incoming program can be stored either in the network disk or PVR so

that the system tries to reduce the redundancy of storing same program in the system.

Particularly, this scheme can work effectively when the size of scalable architecture

becomes large since it will be much likely to store duplicated programs redundantly due

to accommodating many more PVR users.

94

In the third part of this dissertation, we have suggested the practically constructible

multiple-loop network architecture since the initially suggested CG-based mutiple-loop

architecture requires at least nC2 shared disks for the purpose of topology connectivity,

although it has showed the minimum topology-caused traffic to the whole system, thus

providing the best scalability. Nevertheless, it has to be considered that the CG topology

may cause too many shared disks especially when adding more loops, i.e, the increase

of shared disks is proportional to n2, where n is the number of deployed loops.

Moreover, the inserted shared disks necessarily cause the modification of the

deployed loop shape, resulting in an increase in loop size. This implies that a longer

fiber channel is needed when extending into a multiple-loop network architecture using

shared disks. It is expected that the performance of the fiber channel-arbitration loop

(FC-AL) decreases when the loop size increases, although it is believed that the FC-AL

can cover up to 10 km.

In addition, the real cost is an important factor when constructing an actual network

infrastructure, i.e, how many shared disks are needed and how long the physical fiber

channel is required. In other words, the actual cost has to be taken into account as well

as the scalability.

Therefore, the practically constructible network architecture, named MSG

architecture, has been explored as the organization of a multiple-loop architecture,

which can make balance between the actual architecture cost and the system

performance. We has shown that the proposed architecture reveals its superiority by the

trade-off between the CG topology architecture and the MST topology architecture. In

fact, the MSG architecture exposes the constant average loop size similar to the MST

and the close total traffic in addition to acceptable reject ratio compared to the CG.

I believe that there are still more issues that we need to address to further improve

the performance of multimedia-enabled network architecture.

95

First of all, it is conceivable to apply a cache function in the system. Specifically,

we can develop the network disks as cache storage where a proper program can store

in advance and a program can hold longer than other programs based on the cache

strategy. Thus, the system can determine a program access pattern by which users

follow to watch a program. Similar to this, we have introduced the Zipf distribution for the

storage saving scheme in Chapter 3. However, we can further consider various access

patterns such as the Pareto distribution or a given user access profile. Moreover, we

can also think of the relationship between the number of network disks and the number

of PVR users. In order to achieve a target performance using the cache function on

the network disks, we can determine the number of network disks. That is, in a given

number of loops, we can accommodate much more PVR users while minimizing the

number of network disks.

In addition, the current realtime multimedia services can also apply to our system.

Particularly, the IPTV services are now spreading into many more homes, especially

integrated with high-speed Internet services and IP telephone services, known as

Triple-play services. This is possible because the fiber lines are deploying to support

various broadband network services. Nevertheless, the current IPTV services are mainly

considering the delivery of high-quality TV contents via the fiber lines. However, we

can further take into account that our system environment can be applicable in the

IPTV services so that every IPTV user can share their contents with others in the IPTV

network architecture. Furthermore, various IPTV services including bidirectional Internet

services while watching a TV program can be also applicable to our system. That is, the

integration of IPTV services and our system environment can be the basis of our future

work.

96

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BIOGRAPHICAL SKETCH

Sungwook Chung was born in Pusan, Republic of Korea, in 1976. He received

a B.S. in computer science from Sogang University, Korea, in 2002 and an M.S. from

the Computer and Information Science and Engineering (CISE) Department of the

University of Florida in 2005. Since 2005, he has been conducting research with Dr.

Jonathan C.L. Liu in the CISE department at the University of Florida. His research

interests include distributed multimedia systems, home and community area networks

and architectures, storage area networks, high-quality multimedia content distribution,

multimedia consumer devices, and IPTV services.

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