Network Virtualization as a Mean for Service Convergence for Future Communication Systems – What can we learn from Federated Experimental Facilities? K

Network Virtualization as a Mean for Service Convergence for Future Communication Systems –What can we learn from Federated Experimental Facilities?

K. Tutschku ([email protected])

Chair of Future CommunicationProf. Dr. K. Tutschku

Institute for Multimedia and Distributed SystemsFaculty of Computer Science

Future Internet?

?

Overview

The Internet under pressure

The success of the Internet

Network virtualization: virtual structures for convergent services

The GENI experimental facility

Performance issues of Transport Virtualization

Conclusion

Accessnetworks

Core networks

Internet under Pressure

Internet will become a network of applications, services und content

Services are the new central elements Convergence in usage

What changes hereof are anticipated for users, mechanisms and the future network architectures?

GSM

Teletext

Data service

Serviceprovider

Networkoperator

Services

Applications

class. national PTT

POTS Mobile

ISDN

Voice(wired)

Voice(cellular)

Reseller AReseller A

X.25 / FR

Networks under Change: Services

Limited convergence

GPRS

Web

IP service

Serviceprovider

Networkoperator

Services

Applications

A B C D E

IP Service Provider

POTS mobile

ATM/ MPLS

Limit convergence Internet Protocol (IP) is main converging layer


Deficiencies of the Current Internet

Performance (“World wide wait”)

However: No convergence; QoS islands with are available (depending on technology and provider)

Reliability:

Again: no convergence Availability of the Internet ´03: 93.2% − 99.6% Availability of POTS: 99.99% – 99.999% However: sophisticated resilience mechanisms available

at experienced ISP

Competition / business models:

J. Crowcroft: “… I can go on the web and get my gas, electricity, … changed , why is it not possible to get a SPOT price for broad-band internet?” (E2E-interest mailing list on April 26th, 2008); contracts prohibit change

No convergence; even technically infeasible

UMTS

Web. Unified communication appl.

IP Service

xDSL

WLAN

PSTN

Serviceprovider

Networkprovider

Services

Applications

Multi-Network Services

Voice

Overlays (e.g. Skype)

VideoMessa-

gingData

Limit convergence

Internet Protocol (IP) is main converging layer (but: hour glass model!)

Integration of different technical and administrative domains by virtual networks: Overlays Overcome deficiencies and implement new features Networks/overlays have to be (self-)organized for the services

A B C D E

IP Service Provider


Services will be offered and controlled from the edge („edge-based services“) Central services will be virtualized Boundaries between consumer and provider vanish (“prosumer”)

Symmetrical rolls require new architectures (ADSL?) and permit new business models („Peer productivity“)

Management of edge-based services? Optimal placement? Different user behavior? Dimensioning?

Which functions should be self-*?

?

?

provider at edge of network

Data/

Service

distributed centralized

Network-based provider (server)D

ata/ S

ervice

Data/

Service

Data/

Service

Data/

Service

Data/

Service

consumer at edge of network


Application-oriented and self-organizing overlays outperform current services

Support for resources contribution by arbitrary users: „Overlays for Cooperation/ Participation“

What is the performance of self-*? Scalability? Churn? Dynamical traffic patterns?


Head-quarter

ATME3

Management plane

Remote office

Servicerequest (FAX, Web)

„semi-manual“ provisioning

Networks under Change: Transport Systems

Control Plane

Head-quarter

IP layer

100GE layer

DWDM layer

EPON

auto. provisioning

Management Plane

State-of-the-art optical transport systems: Ultra-high transmission capacities; embedding of different transport network into one

physical network (multi-layer networks) Decay of CAPEX per Bit Increased automation self-* features (self-operation,

self-organization)

However: higher complexity („numerous overlays“?) How to achieve convergence?

auto. Signaling

Remote office

Multi-Layer-Networks

Networks under Change: Transport Systems

Success of the Current Internet

Efficient P2P-based, self-organizingcontent distribution networks

Ratio of data traffic types at

public access node

Data traffic by IP TV

P2P, 67,3%

eMail, 1,2%

FTP, 0,3%

other, 23,3%Web, 7,9%Quelle: Telefonica (2003)

Terrabytes per month

YouTube − world wide (Cisco est., May 2008) 100.000

P2P Video streaming in China (Jan. 2008) 33.000

YouTube − USA (Mai 2008) 30.500

US. Internet back bone at year end 2000 25.000

US. Internet back bone at year end 1998 6.000

Quelle: CISCO (2008)

Multi-Source Download (eDonkey, BT)

Publish X

Publish X

Que

ry X

Transfer of segment A

Offers file X

Offers file X

Peer

Index server

Looking for X

Que

ry X

Transfer of segment B

Publish X

Offers file X

P2P: two overlays (virtual structures) with different application layer functions (two basic P2P functions: searching / content exchange); each with different topology, addressing, and routing

Search function: able of self-contained re-organization of search mechanism

Downloading peer: self-initiated selection of providing peer (parallel routing of content) based on resource quality (throughput) select the best (multi-)path for the content

→ Self-operation of basic P2P functions among networks convergence is possible

Diversity I: Multi-Provider Environment

High diversity wrt. paths: Three North-american nation-wide ISPs

Tier1 (AS 3967 Exodus, AS3356 Level3, AS6467 Abovenet; M. Liljenstam et al., 2003)

Multiple routes for increased

resilience and compe-

tition are (theoretically) readily

available!

Network selection not available in

current IP no convergence

Any way: autonomous identi-fication

of available resources needed (Thanks to Michael Menth für vsualization)

East coastWest coast

Diversity II: Multi-Quality Environment

25% of paths violate the triangle inequality (wrt. packet delay) Measurements in PlanetLab by

S. Banerjee et al. (2004)

➞ Internet routing is far from optimal ➞ Better paths exist; capazity is readily

available ➞ Can be offered (competition)

➞ Again: autonomous identification of available resources needed

! „Multi-homing“ not really available current IP protocols

A

Triangle Inequality (TI): D(A,C) ≤ D(A,B) + D(B,C)

B C

direct connection

Using an intermediate

Virtualization of Operating Systems

One hardware executes multiple systems

Safe: Strong isolation of resources, e.g. for testing and debugging

Individual and powerful: User see whole computing center as his own computer

Efficient: reduction of CAPEX (consolidation of multiple machines in a single

physical one) and OPEX (operational issue) Convergence of operating systems

Virtual Networks for Convergent Services

Build a „personal network (PN)” for an application (PN PC) Integration of different technologies and administrative domains Re-use of generic infrastructure on small time scale Push application-layer mechanisms safely down the stack

☝ Avoid “multi-layer” trap autonomic/self-* operation; particularly smart resource mgmt

Convergence by Network Virtualization

Diversity Exploit diversity of resources by smart

localizationProvide optimal resources

OS virtualization Strong isolation of resources Consolidation and efficient

operation Enables local convergence

Overlays Overlays: application-oriented topology,

addressing, and routing Multi-Network Services Self-operation of functions Enables global convergence

Transfer von Segment A

Stellt X zurVerfügung


Peer

Index server

Sucht X

Transfer vonSegment B


Share Virtual Machine

Guest OS

Virtual CPU

Virtual Memory

Virtual I/O

CPU Memory I/O

Virtual Machine Monitor

Guest OS

Virtual Machine

Service Service

Aggregation Load BalancerService

Logical Virtual Server

Load Balancer

Switch

Physical Server

A Formal Description for Virtualization

Virtual resources

Generation of logical resources Sharing: one physical, multiple logical resources Aggregation: one logical, multiple physical

Transport Virtualization (TV)

Example: Virtual Memory OS integrates disconnected physical memory, even disk space,

into continuous memory location of physical memory doesn’t matter

Transport Virtualization (Tutschku, Nakao, 2008): abstraction concept for data transport resources

Physical location of transport resource doesn't matter (as long resource is accessible)

Achieved by: abstract data transport resources combined from one or more physical/overlay transport

resources, e.g. leased line, wave length path, an overlay link, MPLS path, or an IP forwarding capability

physical resources can be used preclusive or concurrently basic resources can be located in even different physical

networks or administrative domains

A. Nakao

T. Zinner, P. Tran-Gia

Concurrent Multi-Path Transfer

Physical topology

Overlays of provider I

Overlays of provider II

Aim: Very high and reliable throughput between two end hosts

Aim: Very high and reliable transmission between two end hosts

Solution: Transport Virtualization:Combine multiple paths (even from different

overlays)

pooled transport pipe

POP

1

Path Source

2

3

SORA Router (One-(Overlay)-Hop)

Internet Router

4

Path oracle One-hop Source Router (SOR)

Routing Overlay (= P2P Multi-Source Download)

Implementation: routing overlays

Gummadi et al (2004): Scalable “One-Hop” (= intermediate) routing overlays

Nakao, Tutschku, Zinner: Consideration of multiple paths

(2008)

! May be inefficient Reduction of overhead (since edge-based) Placement of NV router in core

Application: Transport System Virtualization for high-capacity transmissions, e.g. for HD TV

How can we test it?

1 Divert selected endhost packets

2 Request Paths for Diverted Packets

3 Encapsulated, send using path

4 Decapsulate, egress to destination

Started in 2007

Original agenda Research:

○ Identify fundamental questions; Drive a set of experiments to

validate theories and models Experiments & requirements

○ Drives what infrastructure and facilities are needed

Currently One very rough blueprint; Five different control

architecture

Major ideas infrastructure operation: Clearing house: settles usage request Lifetime for resources: has to be returned at prede-

fined lifetime

GENI: The Global Environment for Network Innovation

Appealing Idea: Federation

Backbone #2ComputeCluster #1

Backbone #1

ComputeCluster #2

Wireless #1

Wireless #2

Access #1

CorporateGENI suites

Other-NationProjects

Other-NationProjects

My experiment runs across the evolving GENI federation.

NSF parts of GENI

My GENI Slice

(Slide by Chip Elliot)

What resources can I use?

Components

Aggregate AComputer Cluster

Components

Aggregate BBackbone Net

Components

Aggregate CMetro Wireless

Offer

GENIClearinghouse

Researcher

Aggregates publish resources, schedules, etc., via clearinghouses

Resource Discovery


GENIClearinghouse

Components


Components


Components


Create my slice

Clearinghouse checks credentials & enforces policyAggregates allocate resources & create topologies

Slice Creation


Components


Components


Components


Experiment – Install my software,debug, collect data, retry, etc.

GENIClearinghouse

Researcher loads software, debugs, collects measurements

Experimentation


Components


Components


Components


Make my slice bigger !

GENIClearinghouse

Allows successful, long-running experiments to grow larger

Slice Growth & Revision


Components


Components


Components


Make my slice even bigger !

GENIClearinghouse

Components

Aggregate DNon-NSF Resources

FederatedClearinghouse

Growth path to international, semi-private, and commercial GENIs

Federation of Clearinghouses

(Slid

e b

y C

hip

Elli

ot)

Components


Components


Components


GENIClearinghouse

FederatedClearinghouse

Components

Aggregate DNon-NSF Resources

Always present in background for usual reasonsWill need an ‘emergency shutdown’ mechanism

Oops

Stop the experimentimmediately !

Operations & Management

(Slid

e b

y C

hip

Elli

ot)

Routing Overlay

usedpath

Routing Overlay

pooledressource

Routing Overlay I

Routing Overlay II

pooledressource

Federation for Transport Virtualization

Path selection

Path selection for concurrent use

Path selection in federated networks convegence of networks

Transmission Model

p1,1

dst

Assumption: use k parallel paths on m overlays

p1,n1

pm,1

pm,nm

src k pooled paths

m

i ink1With paths

Data stream divided at router into segments with k parts

1

k

2

k parts have arrived

k parts are send in parallel at time t

k-1

each provider will offer a set ni of parallel paths(i = 1…m)

1

k

overlay 1

overlay m

Buffer occupancy?

Reassemble data stream from obtained parts

Re-sequencing buffer of size L

Scheduling?

So far: Simulation Experiment

Input:

Number of paths

Scheduling

Output: Re-sequencing buffer occupancy distribution

Search for path selection strategies; future on-line selection for convergence

Path delay distributions

Path capacity

Source Destination

Impact of Type of Delay Distribution I

Types of distributions:

Uniform: artificial behavior

Truncated Gaussian: mathematical tractability

Bimodal: two modes of a path

Investigation of different influence factors

Delay

Impact of Type of Delay Distribution II

Two synchronous, equal capacity paths Three synchronous, equal capacity paths

Buffer

Highly non-linear careful and complex path selection

Buffer

Current Work: Perform Real-World Measurements

Measurement set-up

Gain realistic parameters and strategies

Conclusion

Expected features of the Future Internet Faster, more reliable, more business cases, increased interaction

with users: symmetric rolls, „Architecture for Participation“ Forming of applications-specific overlays

Network virtualization: Consolidation of multiple (virtual) network into one physical

infrastructure

Making data transport independent from resource locations transport virtualization

Integration/convergence of different transport systems und operator domains by overlays and network virtualization

Design networks for applications (rather than designing applications for networks)

Experimental facilities: Federation: blue print for future network operation and

convergence Resources with limited lifetime significant challenges in

resource management

Thanks for your

attention!

Questions?

Documents

Network Virtualization as a Mean for Service Convergence for Future Communication Systems – What can we learn from Federated Experimental Facilities? K