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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
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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 • 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
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
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.
SocialSwam Design Goal • Maximize collaboration between social peers • Maintain game-based techniques to encourage the cooperation of non-social peers
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
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
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
Approximating SocialDistance Between Indirect Peers -------- direct peers Peers beyond this value are considered as non-social
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:
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
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
Conclusion • Typical incentives create inefficiencies • SocialSwarm exploits SocialDistance to reduce this inefficiencies • The “gather-and-share” technique achieve better performance
