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Quality of Service in Peer-to-Peer Media Streaming. Darshan Purandare University of Central Florida Orlando, FL, USA. Outline. Peer-to-Peer (P2P) Media Streaming Related Work Current Issues Our Proposed Methodology Alliance Theory Important P2P Media Streaming Metrics

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Quality of service in peer to peer media streaming

Quality of Service in Peer-to-Peer Media Streaming

Darshan Purandare

University of Central Florida

Orlando, FL, USA


Outline
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
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
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
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

  • Content Distribution Tool

1

Server

3

2

5

4

1

  • File is chopped into pieces

3


Major approaches
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
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

Application Layer Multicast

Peer-to-Peer

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

Tree Based

Mesh Based

[NICE, ZigZag, SpreadIT]

[ESM, Narada]


Application layer multicast alm
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
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.




Peer to peer streaming models
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
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


P2p based streaming model

1

Server

3

2

5

4

1

3

P2P Based Streaming Model


Metrics
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
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 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
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
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
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
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
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
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

POWER

Server

POWER

POWER

Alliance 1

Alliance 2

Alliance 3

Alliance 4


Alliance theory
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 functionality
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



Small world network
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


Comparison with random graphs
Comparison with Random Graphs

  • Total Node = 512

  • Node Degree = 8

  • High clustering coefficient signifies node connectivity in the vicinity


Simulation details
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











Results and discussion
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
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


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