Quality of Service in PeertoPeer Media Streaming

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Quality of Service in Peer-to-Peer Media Streaming

• Darshan Purandare
• University of Central Florida
• Orlando, FL, USA

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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

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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

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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

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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

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P2P Sharing

• Content Distribution Tool

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Server
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• File is chopped into pieces

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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

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Content Distribution Networks (CDNs)

• CDN nodes deployed in multiple locations, often
  over multiple backbones
• These nodes cooperate with each other to satisfy
  an end users 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

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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

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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.

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Tree Based ALM
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Mesh Based ALM
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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

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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
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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Alliance Formation
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Alliance Formation
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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

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Alliance Functionality
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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

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Comparison with Random Graphs

• Total Node 512
• Node Degree 8
• High clustering coefficient signifies node
  connectivity in the vicinity

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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

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Number of Nodes vs Jitter Factor
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Number of Nodes vs Latency
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Number of Nodes vs uplink utilization
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Bit rate vs Jitter Factor
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Bit rate vs Latency
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Fairness Num of Nodes vs Share Ratio
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Node Failure vs Jitter Factor
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Node Failure vs Average Latency
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Number of Nodes vs Control Overhead
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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

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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