1 / 29

SocialSwarm: Exploiting Distance in Social Networks for Collaborative Flash File Distribution

SocialSwarm: Exploiting Distance in Social Networks for Collaborative Flash File Distribution. Presented by: Su Yingbin. Outline. Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion. Tit-for-tat as incentive to upload. Want to encourage all peers to contribute

sun
Download Presentation

SocialSwarm: Exploiting Distance in Social Networks for Collaborative Flash File Distribution

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. SocialSwarm: Exploiting Distance in Social Networks for Collaborative Flash File Distribution Presented by: Su Yingbin

  2. Outline • Introduction • SocialSwam Design • Notations • Algorithms • Evaluation • Conclusion

  3. Tit-for-tat as incentive to upload • Want to encourage all peers to contribute • Peer A said to choke peer B if it (A) decides not to upload to B • Each peer (say A) unchokes at most 4 interested peers at any time • The three with the largest upload rates to A • Where the tit-for-tat comes in • Another randomly chosen (Optimistic Unchoke) • To periodically look for better choices

  4. Typical BitTorrent incentives create inefficiencies • Clients typically avoid increasing the number of unchoke slots • Bandwidth reserved to peers won’t actually be used totally. • Social hubs can’t receive the highest priority in receiving file

  5. Karame et al. show that combining locally optimal solutions of the smaller social teams would give a globally optimal solution for the entire social network.

  6. Just work as a team!

  7. SocialSwam Design Goal • Maximize collaboration between social peers • Maintain game-based techniques to encourage the cooperation of non-social peers

  8. SocialSwarm Interaction Overview Retrieve social peers and non-social peers from tracker Identifies Bob’s social peers Coordinates chunk collection with them Altruistically shares bandwidth with them Interact with each other as well as standard BitTorrent clients

  9. How ? • How to identify social peers and non-social peers ? • Social Distance • How to collaborate with each other among a social group as well as non-social peers ? • Adaptive Bandwidth Allocation • Chunk Prioritization • Optimistic Unchoke Candidate Selection

  10. Notations

  11. Altruism Between Direct Social Peers • I(a, b) is the number of reciprocal interactions a has had within a given time window with b • I(a, all) is the number of reciprocal interactions a has had with all of its peers during • the same window of time. • A(a, b) represents the proportional willingness that a peer a has to share resources with each of its direct peers

  12. Approximating SocialDistance Between Indirect Peers -------- direct peers Peers beyond this value are considered as non-social

  13. Notations

  14. Overall Rarity for Each Given Chunk

  15. Social Rarity for Each Given Chunk

  16. Non-social Rarity for Each Given Chunk

  17. The “gather-and-share” Technique • From the social group perspective • When the average social rarity for all chunks is high, allocate more bandwidth for non-social peers. • As the average social rarity for all chunks decreasing, allocate more bandwidth for social peers. • Average social rarity for all chunks: • Maximum percentage of bandwidth allocated to social peers:

  18. The “gather-and-share” Technique • From the social individual perspective • Chunk prioritization • Optimistic Unchoke Candidate Selection combines the social, non-social, and overall rarities to form a combined weighted rarity for each given chunk target a peer with the largest group of rare chunks at each time interval ti

  19. SocialSwarm in a Nutshell

  20. Social Network Data Set • 500 nodes with their interactions – Wall Postings – extracted from Facebook • Each pair of reciprocal postings is considered a single interaction. • Interactions are used to determine the direct level of altruism between Facebook users. • Beyond MaxSocialDistance are considered as non-social peers

  21. Baseline Test Parameters

  22. Comparison of Basic Download Time

  23. Client Download Rate Comparison

  24. Chunk Rarity Reduction Comparison

  25. Effect of File Size on Peer Throughput

  26. Effect of Maximum SocialDistance on Peer Throughput

  27. Effect of Additional Seed Capacity

  28. Bandwidth Contribution and Unchoke Slot Allocation

  29. Conclusion • Typical incentives create inefficiencies • SocialSwarm exploits SocialDistance to reduce this inefficiencies • The “gather-and-share” technique achieve better performance

More Related