1 / 35

Natural Selection in Peer-to-Peer Streaming: From the Cathedral to the Bazaar

Natural Selection in Peer-to-Peer Streaming: From the Cathedral to the Bazaar. Vivek Shrivastava, Suman Banerjee University of Wisconsin-Madison, USA ACM NOSSDAV’05. imposed incentive/rule. natural incentive. Cathedral and Bazaar. Cathedral. Bazaar. Introduction.

webb
Download Presentation

Natural Selection in Peer-to-Peer Streaming: From the Cathedral to the Bazaar

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Natural Selection in Peer-to-Peer Streaming: From the Cathedral to the Bazaar Vivek Shrivastava, Suman Banerjee University of Wisconsin-Madison, USA ACM NOSSDAV’05

  2. imposed incentive/rule natural incentive Cathedral and Bazaar Cathedral Bazaar

  3. Introduction • Peer altruism (resource contribution) is a key factor of p2p applications • BitTorrent employs a tit-for-tat rule • This work exploits scenarios in p2p streaming media applications where resource sharing is a natural behavior without external rules and incentives

  4. Outlines • Background • Proposed Bazaar framework • Evaluation and simulation • Summary

  5. Background • not altruistic • limited asymmetrical upstream bandwidth of DSL and cable modems • uploading may reduce its access bandwidth and degrade the network access performance

  6. This paper • p2p streaming environment has inherent natural incentives for peers to contribute • form efficient overlay tree for data streaming • shift from Cathedral style to Bazaar style, where no rules are imposed on peers and resources sharing takes place naturally as peers try to maintain their perceived data utility

  7. Utility • Utility • benefit(incoming bandwidth, latency) – cost (outgoing bandwidth) • maybe different in different peers

  8. An example all selfish

  9. An example • Case (a) • incoming bandwidth to peer A is 80kbps • Case (b) • incoming bandwidth to peer A is 200kbps • outgoing bandwidth from peer A is 400kbps • If the increase in A’s perceived data utility offsets the loss, A will do so

  10. An example • final overlay depends on the mix of utility functions of peers • peer B also has natural incentive to join under A as the streaming bandwidth increases from 80kbps to 200kbps

  11. This paper • provide a platform to facilitate the formation of such a mutually beneficial overlay without introducing any rules or incentives in the system • based on natural incentive of peer to conserve own incoming bandwidth by attracting the new entrant to join under itself rather than its parent

  12. Bazaar framework • regular peers • strategic, maximize incoming and minimize outgoing bandwidth • BSE (Boot Strap Entity) • ~tracker, provide overlay information to new node • root (publisher) • altruistic, allocates fixed outgoing bandwidth during the entire streaming session

  13. Market Quote (M-Quote) • Each node provides a M-Quote of its services to attract peers to join under it rather its parent, so as to preserve its own incoming bandwidth • has the following advertised components: • bandwidth • latency from root to the peer • kept in BSE

  14. Bazaar framework • peers can perform • join/leave the overlay dynamically • advertise their services • participate in shuffle operation to improve overlay structure

  15. Bazaar in action 3. revise M-Quote (250 = 500/2) 1. root enters the system 4. if A is selfish, it can send M-Quote of 0 or do notsend at all. However, a newnode will definitely choosesroot as peer which reduce the bandwidth shared by A. This motivates A to offer competitive quote 2. A interested in the content joins the system

  16. Bazaar in action B choose A if bandwidth outweighs latency B choose A if latency outweighs bandwidth Advertising a lower outgoing bandwidth which leads to competition of parent bandwidth, A may participate in a local shuffling operation

  17. Bazaar in action shuffle is a periodic operation if B is attracted by the new quote,the overlay changes accordingly

  18. Evaluation • Utility function Y. hua Chum J. Chuang, and H. Zhang, “A case for taxation in peer-to-peer streaming broadcast,” Workshop on Practice and theory of incentives in networked systems (PINS), 2004

  19. Simulation environment • peer-to-peer network simulator myns, developed at University of Maryland • use Transit Stub topology generated by GT-ITM topology generator • 50 peers, all results are averaged over 1000 permutations of peer join order

  20. Evaluate under modes of operation

  21. Performance metrics • throughput • incoming bandwidth of each peer • total system utility • sum of perceived data utility of all peers

  22. Bimodal simulations • all peers are categorized as high or low capacity peers • high capacity: outgoing bandwidth randomly selected from 500Kbps to 1Mbps • low capacity: random [50Kbps, 450Kbps] • simulate heterogeneous peer environments by varying fraction of high capacity peers from 0 to 1 • max streaming rate is 500Kbps

  23. Bimodal simulations – system utility

  24. Bimodal simulations – system utility • Observations • utility increases with fraction of high capacity, as cost of forward (fraction of forward bandwidth over max outgoing bandwidth) reduces • strategic is better than random, as random my degrade it own and others utility

  25. Bimodal simulations – throughput

  26. Bimodal simulations – throughput

  27. Bimodal simulations – throughput • Observation • gap between altruistic and strategic mode decreases with decrease in fraction of high capacity peers • The fraction of high capacity peers in real life is low, so strategic peers can achieve good throughput in real life

  28. Trace based simulations • outgoing capacity distribution of peers are based on traces collected from Sigcomm, Slashdot and Gnutella • max streaming rate is 500Kbps A. Bharambe, S. Rao, V. Padmanabhan, S. Seshan, and H. Zhang, “The impact of heterogeneous bandwidth constraints on DHT-based multicast protocols,” International Workshop on P2P Systems, 2005

  29. Trace based simulations varying the fraction of strategic peers

  30. Trace based simulations

  31. Trace based simulations • Observations • Sigcomm: performance of Bazaar degrade substantially as the fraction of strategic peers increases • Slashdot and Gnutella: performance degrade gracefully • infer Bazaar framework is particularly well suited for many p2p streaming scenarios, in which peers are mostly resource poor

  32. Summary • Bazaar framework • make use of the natural inherent incentive • facilitate formation of efficient overlay structure • improve performance in p2p streaming applications involving strategic peers • optimize by shuffle-k operations • works well in environments with low fraction of high capacity peers

  33. THE END

  34. Cathedral approach • most existing p2p streaming impose rules and incentives to motivate contribution • each peer is expected to follow – Cathedral approach

  35. Trace-based • Sigcomm: streaming Sigcomm conferences or workshops; most audience were interested in the contents but could not attain in person • Slashdot: a popular web-based discussion forum; audience is either interested in the contents or curious about the system • Gnutella: hosts in the Gnutella system CHU ET. AL, “Early deployment experience with an overlay based internet broadcasting system,” USENIX Annual Technical Conference, June 2004

More Related