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Quality of Service in Peer-to-Peer Media Streaming

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  • 1. Quality of Service in Peer-to- Peer Media Streaming Darshan Purandare University of Central Florida Orlando, FL, USA
  • 2. 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
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. P2P Sharing  Content Distribution Tool … 1 … Server 3 2 … … … 5 4 … … … 1  File is chopped into pieces 3
  • 7. 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
  • 8. 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
  • 9. Media Streaming Application Layer Peer-to-Peer Multicast [CoolStreaming, PPLive, SO PCast,TV Ants, Feidian] Tree Based Mesh Based [NICE, ZigZag, SpreadIT] [ESM, Narada]
  • 10. 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
  • 11. 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.
  • 12. Tree Based ALM
  • 13. Mesh Based ALM
  • 14. 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
  • 15. 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
  • 16. P2P Based Streaming Model … 1 … Server 3 2 … … … 5 4 … … … 1 3
  • 17. 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
  • 18. 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
  • 19. 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
  • 20. 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
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. 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
  • 25. 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
  • 26. POWER POWER Server POWER POWER Alliance 1 Alliance 2 Alliance 3 Alliance 4
  • 27. 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
  • 28. Alliance Formation
  • 29. Alliance Formation
  • 30. 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
  • 31. Alliance Functionality
  • 32. 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
  • 33. Comparison with Random Graphs Graph Type Server Distance Clustering Coefficient Random 3.16 0.013 BEAM 3.19 0.42 •Total Node = 512 •Node Degree = 8 •High clustering coefficient signifies node connectivity in the vicinity
  • 34. 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
  • 35. Number of Nodes vs Jitter Factor
  • 36. Number of Nodes vs Latency
  • 37. Number of Nodes vs % uplink utilization
  • 38. Bit rate vs Jitter Factor
  • 39. Bit rate vs Latency
  • 40. Fairness: Num of Nodes vs Share Ratio
  • 41. Node Failure vs Jitter Factor
  • 42. Node Failure vs Average Latency
  • 43. Number of Nodes vs Control Overhead
  • 44. 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
  • 45. 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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