Quality of Service in Peer-to-Peer Media Streaming

Darshan PurandareUniversity of Central FloridaOrlando, FL, USA

Outline Peer-to-Peer (P2P) Media Streaming Related Work Current Issues Our Proposed Methodology Alliance Theory Important P2P Media Streaming Metrics Improving Locality of Traffic Security Issues Future Trends

Introduction Advent of multimedia technology and broadband surge

lead to: Excessive usage of P2P application that includes:

Sharing of Large Videos over the internet Video-on-Demand (VoD) applications P2P media streaming applications

BitTorrent like P2P models suitable for bulk file transfer P2P file sharing has no issues like QoS:

No need to playback the media in real time

Downloading takes long time, many users do it overnight

Introduction Contd.

P2P media streaming is non trivial: Need to playback the media in real time

Quality of Service Procure future media stream packets

Needs reliable neighbors and effective management High “churn” rate – Users join and leave in between

Needs robust network topology to overcome churn Internet dynamics and congestion in the interior of the network

Degrades QoS Fairness policies extremely difficult to apply like tit-for-tat

High bandwidth users have no incentive to contribute

P2P Media Streaming Media streaming extremely expensive

1 hour of video encoded at 300Kbps = 128.7 MB Serving 1000 users would require 125.68 GB

Media Server cannot serve everybody in swarm In P2P Streaming:

Peers form an overlay of nodes on top of www internet Nodes in the overlay connected by direct paths (virtual or logical links), in

reality, connected by many physical links in the underlying network Nodes offer their uplink bandwidth while downloading and viewing the

media content Takes load off the server Scalable

P2P Sharing

Server

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Content Distribution ToolContent Distribution Tool

File is chopped into File is chopped into piecespieces

Major Approaches

Major approaches Content Distribution Networks like Akamai

Expensive Only large infrastructure can afford Client Server Model

Not scalable Application Layer Multicast

Alternate to IP Multicast Peer-to-Peer Based

Most viable and simple to use and deploy No setup cost Scalable

Content Distribution Networks (CDNs)

CDN nodes deployed in multiple locations, often over multiple backbones

These nodes cooperate with each other to satisfy an end user’s request

User request is sent to nearest CDN node, which has a cached copy

QoS improves as end user receives best possible connection

Yahoo mail uses Akamai

Media Streaming

Tree Based

Application Layer Multicast

Peer-to-Peer

Mesh Based

[CoolStreaming, PPLive, SOPCast,TV Ants, Feidian]

[NICE, ZigZag, SpreadIT] [ESM, Narada]

Application Layer Multicast (ALM) Very sparse deployment of IP Multicast due to technical

and administrative reasons In ALM:

Multicasting implemented at end hosts instead of network routers Nodes form unicast channels or tunnels between them Overlay Construction algorithms at end hosts can be more easily

applied End hosts needs lot of bandwidth

Most ALM approaches form Tree based topology: Simple to use Ineffective in case of churn and node failures as incurs high

recovery time

ALM Methodologies Tree Based

Content flows from server to nodes in a tree like fashion, every node forwards the content to its children, which in turn forward to their children

One point of failure for a complete subtree High recovery time Notes Tree Base Approaches: NICE, SpreadIT, Zigzag

Mesh Based Overcomes tree based flaws Nodes maintain state information of many nodes High control overhead Notes Mesh Based approaches include Narada and ESM from CMU.

Tree Based ALM

Mesh Based ALM

Peer-to-Peer Streaming Models Design flaws in ALM lead to current day P2P Streaming models based

on chunk driven technology Media content is broken down in small pieces and disseminated in the

swarm Neighboring nodes use Gossip protocol to exchange buffer

information Nodes trade unavailable pieces Robust and Scalable Most noted approach in recent years: CoolStreaming

PPLive, SOPCast, Fiedian, TV Ants are derivates of CoolStreaming Proprietary and working philosophy not published Reverse Engineered and measurement studies released

CoolStreaming

Files is chopped by server and disseminated in the swarm Node upon arrival obtain a peerlist of 40 nodes from the

server Nodes contact these nodes for media content In steady state, every node has typically 4-8 neighbors, it

periodically shares it buffer content map with neighbors Nodes exchange the unavailable content Real world deployed and highly successful system

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P2P Based Streaming Model

Metrics Quality of Service

Jitter less transmission Low end to end latency

Uplink utilization High uplink throughput leads to scalable P2P systems

Robustness and Reliability Churn, Node failure or departure should not affect QoS

Scalability Fairness

Determined in terms of content served (Share Ratio) No user should be forced to upload much more than what it has

downloaded Security

Implicitly affects above metrics

Quality of Service Most important metric Jitter: Unavailability of stream content at play time causes

jitter Jitter less transmission ensures good media playback Continuous supply of stream content ensures no jitters Latency: Difference in time between playback at server

and user Lower latency keeps users interested

A live event viz. Soccer match would lose importance in crucial moments if the transmission is delayed

Reducing hop count reduces latency

Uplink Utilization

Uplink is the most sparse and important resource in swarm

Summation of uplinks of all nodes is the load taken off the server

Utilization = Uplink used / Uplink Available Needs effective node organization and topology to

maximize uplink utilization High uplink throughput means more bandwidth in the

swarm and hence it leads to scalable P2P systems

Robustness and Reliability

A Robust and Reliable P2P system should be able to support with an acceptable levels of QoS under following conditions: High churn Node failure Congestion in the interior of the network

Affects QoS Efficient peering techniques and node topology ensures

robust and reliable P2P networks

Scalability

Serve as many users as possible with an acceptable level of QoS

Increasing number of nodes should not degrade QoS An effective overlay node topology and high uplink

throughput ensures scalable systems

Fairness

Measured in terms of content served to the swarm Share Ratio = Uploaded Volume / Downloaded Volume

Randomness in swarm causes severe disparity Many nodes upload huge volume of content Many nodes get a free ride with no or very less contribution

Must have an incentive for an end user to contribute P2P file sharing system like BitTorrent use tit-for-tat policy to stop

free riding Not easy to use it in Streaming as nodes procure pieces in real time

and applying tit-for-tat can cause delays

Security

Implicitly affects other P2P Streaming metrics Mainly 4 types of attacks:

Malicious garbled Payload insertion Free rider – Selfish used only downloads with no uploads Whitewasher – After being kicked out, comes again with new

identity. Such nodes use IP spoofing DDoS attack – One or more nodes collectively launch a DoS

attack on media server to crack the system down Lot of attack on P2P file sharing system but very few on

Streaming Possibility cannot be denied

Current Issues High buffering time

Half a minute for popular streaming channels and around 2 minutes for less popular

Some nodes lag with their peers by more than 2 minutes in playback time. Better Peering Strategy needed

Uneven distribution of uplink bandwidths (Unfairness) Huge volumes of cross ISP traffic

ISPs use bandwidth throttling to limit bandwidth usage Degrade QoS perceived at used end

Sub Optimal uplink utilization

Our Proposed Methodology BEAM: Bit stEAMing Swarm based P2P model

Uses Alliance theory for peering Nodes cluster in small groups of 4-6 to form an alliance High contributing nodes (Power Nodes) have high ranking based

on their share ratios Such nodes may be served directly by server Serves as an incentive mechanism for nodes to contribute Network topology in our model is a small world network In small world networks, every node is connected to every other

node in the swarm by a small number of path length

POWER

Server

Alliance 1

Alliance 2Alliance 3Alliance 4

POWER

POWER

POWER

Alliance Theory

Nodes cluster in groups of 4-6 to form an alliance Alliance members have common trust and treaty

As a node receives new content, it forwards among its alliance members first

Alliance members are mutually trusted All members of an alliance have an active connection with other

members

Applying security policies in alliance is much easier

Alliance Formation

Alliance Formation

Alliance Functionality

A node can be a member of multiple alliances H = Maximum number of nodes in an Alliance K = Maximum number of alliances a node can join As a node procures a new stream packet from other source:

It spreads it in its alliances Forwards different pieces to different nodes Nodes in turn exchange pieces Makes it mandatory for a node to upload the content As new nodes procure content, they forward it in their other alliances H and K impose restrictions on alliance and stop them from growing too

large

Alliance Functionality

Small World Network

Small World Network is characterized by: High coefficient of clustering Mean path lengths comparable to mean path lengths in random

graphs Every node can be reached from any other node in a small

number of hop counters (nearly logN path length) BEAM generate node topology like a small world network

Alliance mandates a high clustering coefficient A node has multiple alliances, i.e. it creates links with far located

nodes Mean path length is near Random graphs

Graph Type Server Distance Clustering Coefficient

Random 3.16 0.013

BEAM 3.19 0.42

Comparison with Random Graphs

•Total Node = 512

•Node Degree = 8

•High clustering coefficient signifies node connectivity in the vicinity

Simulation Details Custom time event based simulator Created in Python on Linux (Ubuntu) platform Comparison with CoolStreaming

Chunk Driven Most popular

Ideal for testing extreme scenarios: Difficulty in obtaining thousands of nodes in real world implementation Planet Lab like testbed overlay are better suites but their numbers are

limited. As of Oct 2006, there are 704 machines hosted on 339 sites Some details abstracted without loss:

Propagation Delay TCP dynamics Shared Bottlenecks

Number of Nodes vs Jitter Factor

Number of Nodes vs Latency

Number of Nodes vs % uplink utilization

Bit rate vs Jitter Factor

Bit rate vs Latency

Fairness: Num of Nodes vs Share Ratio

Node Failure vs Jitter Factor

Node Failure vs Average Latency

Number of Nodes vs Control Overhead

Results and Discussion

BEAM has scaled well and outperformed CoolStreaming in almost all the metrics

Forming alliance has proved to be an effective way to organize the peers

Control overhead is minimal for most combinations of H,K values

QoS is near optimal even in such random swarm environment

BEAM is robust and reliable and delivers excellent performance even under severe churn and node failures

Conclusion

P2P Streaming is an effective way to broadcast with little or no infrastructure

BEAM has proven to be an effective model for P2P media streaming

Alliance theory is a sound peering technique and provides robustness to the system

Security issues needs to be dealt with for DoS attacks