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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
slide6

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
slide10

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
slide27

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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