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

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Page 1: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

SocialSwarm: Exploiting Distance in Social Networks for Collaborative Flash

File Distribution

Presented by: Su Yingbin

Page 2: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Outline

Introduction

SocialSwam Design

Notations

Algorithms

Evaluation

Conclusion

Page 3: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Tit-for-tat as incentive to uploadWant to encourage all peers to contributePeer A said to choke peer B if it (A)

decides not to upload to BEach peer (say A) unchokes at most 4

interested peers at any timeThe three with the largest upload rates to A

Where the tit-for-tat comes inAnother randomly chosen (Optimistic Unchoke)

To periodically look for better choices

Page 4: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Typical BitTorrent incentives create inefficienciesClients typically avoid increasing the number

of unchoke slotsBandwidth reserved to peers won’t actually

be used totally.Social hubs can’t receive the highest priority

in receiving file

Page 5: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

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.

Page 6: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Just work as a team!

Page 7: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

SocialSwam Design Goal

Maximize collaboration between social peers

Maintain game-based techniques to encourage the cooperation of non-social peers

Page 8: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

SocialSwarm Interaction Overview1. 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

Page 9: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

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 AllocationChunk PrioritizationOptimistic Unchoke Candidate Selection

Page 10: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Notations

Page 11: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

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 duringthe 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

Page 12: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Approximating SocialDistance Between Indirect Peers

-------- direct peers

Peers beyond this value are

considered as non-social

Page 13: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Notations

Page 14: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Overall Rarity for Each Given Chunk

Page 15: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Social Rarity for Each Given Chunk

Page 16: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Non-social Rarity for Each Given Chunk

Page 17: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

The “gather-and-share” TechniqueFrom 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:

Page 18: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

The “gather-and-share” TechniqueFrom 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

Page 19: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

SocialSwarm in a Nutshell

Page 20: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Social Network Data Set500 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

Page 21: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Baseline Test Parameters

Page 22: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Comparison of Basic Download Time

Page 23: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Client Download Rate Comparison

Page 24: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Chunk Rarity Reduction Comparison

Page 25: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Effect of File Size on Peer Throughput

Page 26: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Effect of Maximum SocialDistance on Peer Throughput

Page 27: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Effect of Additional Seed Capacity

Page 28: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

Bandwidth Contribution and Unchoke Slot Allocation

Page 29: Presented by: Su Yingbin. Outline Introduction SocialSwam Design Notations Algorithms Evaluation Conclusion

ConclusionTypical incentives create inefficiencies

SocialSwarm exploits SocialDistance to reduce this inefficiencies

The “gather-and-share” technique achieve better performance