Myconet: A Fungi-Inspired Model for P2P Superpeer Overlay Topologies

1. Myconet: A Fungi-Inspired Model forP2P Superpeer Overlay Topologies Paul Snyder, Rachel Greenstadt, and Giuseppe Valetto {pls29,greenie,valetto}@cs.drexel.edu Department of Computer ScienceDrexel University

2. Outline Overview Protocol description Evaluation Conclusion 2

3. Photo credit: K. Fleming. Reproduced under a Creative Commons license. http://www.flickr.com/photos/myriorama/101120710/ Myconet: Self-organization of superpeer overlay topologies • Self-organizing unstructured P2P overlay • Hierarchical superpeers • Inspired by hyphae, the robust, root-like structures of fungal mycelia • Goals • Effective exploitation of peers • Resilience to failures

4. Quick Definitions • Peer-to-peer • Overlay networks • Superpeers • Unstructured P2P

5. Myconet’s Metaphor Mycelium Hyphae Nutrients/Biomass Myconet overlay Superpeers Regular peers

6. Goals of the Myconet Protocol Peers operate with local information only Use multiple protocol states to balance exploration and exploitation Select highest-capacity nodes as superpeers Quick recovery after node failure 6

7. Outline Overview Protocol description Evaluation Conclusion 7

8. Myconet Basics • Round-based simulation in PeerSim • Peers characterized by an integer capacity • Peers with direct hyphal links are considered neighbors • Uses a lower-level overlay to communicate node status information • The gossip-based Newscast protocol

9. All peers begin as disconnected biomass Peers that cannot find a hypha to connect to become extending hyphae (superpeers) Extending hyphae seek biomass and to connect to another hyphal peer Protocol: Extending Hyphae unattached biomass START 3. loses parent hypha 2. finds hypha attached biomass 1. spores extending hypha

10. Protocol: Bootstrapping Round 0 Biomass peers Extending hyphae

11. Protocol: Bootstrapping Round 1 Biomass peers Extending hyphae 11

12. Protocol: Branching Hyphae unattached biomass START 3. loses parent hypha • Extending hyphae with enough biomass promote to branching hyphae, which: • Form inter-hyphal connections • Absorb biomass from extending peers • Regulate number of extending peers in the network • Demote to extending status if unable to maintain biomass 2. finds hypha attached biomass 1. spores 6. absorbed extending hypha 7. promoted by a branching or immobile hypha 5. reaches or exceeds capacity 7. falls below full utilization branching hypha 12

13. Protocol: Superpeer Promotion Biomass peers Extending hyphae Branching hyphae Round 1 13

14. Protocol: Superpeer Promotion Biomass peers Extending hyphae Branching hyphae Round 2 14 14

15. Protocol: Superpeer Promotion Biomass peers Extending hyphae Branching hyphae Round 4 15 15

16. Protocol: Immobile Hyphae Branching hypha with Cn hyphal connections become immobile hyphae In the network long enough to be considered stable Pull biomass from extending and branching hyphae Maintain hyphal connections if lost Regulate growth or collapse of extending and branching hyphae Demote if unable to maintain utilization threshholds unattached biomass START 3. loses parent hypha 2. finds hypha attached biomass 1. spores 6. absorbed extending hypha 7. promoted by a branching or immobile hypha 5. reaches or exceeds capacity 8. absorbed 7. falls below full utilization 11. absorbed branching hypha 9. reaches or exceeds target hyphal link count immobile hypha 10. falls below utilization threshold

17. Protocol: Stability Biomass peers Extending hyphae Branching hyphae Immobile hyphae Round 4

18. Protocol: Stability Biomass peers Extending hyphae Branching hyphae Immobile hyphae Round 17 18

19. Protocol: Stability Biomass peers Extending hyphae Branching hyphae Immobile hyphae Round 22 19

20. Outline Overview Protocol description Evaluation Conclusion 20

21. Convergence to stable configuration Resource utilization Approximation of optimal superpeer configuration Resilience to catastrophic failures Evaluation

22. Evaluation: Methodology • Tested using round-based simulation in PeerSim • Most graphs represent experimental results for networks of 105 nodes, averaged over 25 runs • Experiments were conducted with network sizes from 103 to 106 nodes • Peer capacities were assigned using a power-law distribution • For networks of size of 105, the probability of peer n having capacity x is P[cn = x] = x-2, with x in the interval [1,500] • Maximum capacities were adjusted for other network sizes • Also tested with uniform random distributions, with similar results • Compared performance to SG-1 and ERASP

23. Evaluation: Time to Stability For 105 nodes, Myconet quickly converges to around 225 superpeers

24. Evaluation: Utilization Levels Within 20 rounds, 95% of peers are connected to a branching or immobile hypha

25. Evaluation: Superpeer Configuration The total superpeer count closely tracks the theoretical optimum

26. Evaluation: Failure Recovery Biomass peers Extending hyphae Branching hyphae Immobile hyphae Round 22

27. Evaluation: Failure Recovery Biomass peers Extending hyphae Branching hyphae Immobile hyphae Round 39 27

28. Evaluation: Failure Recovery Biomass peers Extending hyphae Branching hyphae Immobile hyphae Round 40 28

29. Evaluation: Failure Recovery Biomass peers Extending hyphae Branching hyphae Immobile hyphae Round 57

30. Evaluation: Failure Recovery Biomass peers Extending hyphae Branching hyphae Immobile hyphae Round 71 30

31. Evaluation: Failure Recovery The Myconet overlay quickly repairs itself after a catastrophic failure

32. Myconet effectively constructs and maintains a strongly-interconnected, decentralized superpeer overlay Quickly converges to an optimal number of superpeers and high levels of capacity utilization. Performance scales smoothly up to at least 106 peers Compared to other the state of the art, our simulations show Myconet fares well in terms of: Network stabilization Response to catastrophic failure Capacity utilization Evaluation: Bottom Line

33. Outline Overview Protocol description Evaluation Conclusion 33

34. Conclusions • Myconet overlay demonstrates applicability of fungal metaphor to peer-to-peer overlays • Take-aways: • Hierarchical superpeer states were useful in designing self-organizing network dynamics • Choosing to underutilize some superpeers (extending hyphae) useful when balancing exploration vs. exploitation • Increasing Cn parameter increased instant resistance to disconnection, but had unexpectedly slight effects on dynamics

35. Future Work • Examine performance under network churn • Measure overlay maintenance costs • Test the performance of P2P applications running on the Myconet overlay • Dynamic adaptation of Cn parameter • Move from round-based simulation to protocol implementation • Explore possibility of formalizing metaphor in terms of more rigorous biological models

36. Questions? Paul Snyder, Rachel Greenstadt, and Giuseppe Valetto {pls29,greenie,valetto}@cs.drexel.edu

37. References [1] A. Montresor, “A robust protocol for building superpeer overlay topologies” in Proceedings of the 4th International Conference on Peer-to-Peer computing. Zurich, Switzerland: IEEE, Aug. 2004, CONFERENCE, pp. 202-209. [2] W. Liu, J. Yu, J. Song, X. Lan, and B. Cao, “ERASP: An Efficient and Robust Adaptive Superpeer Overlay Network,” Lecture Notes in Computer Science, vol. 4976, p.468, 2008.