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Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion.

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Presentation on theme: "Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion."— Presentation transcript:

1 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 1. Retrieve social peers and non-social peers from tracker 2. Identifies Bob’s social peers 3. Coordinates chunk collection with them 4. Altruistically shares bandwidth with them 5. 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


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