Evolution and Enhancement of BitTorrent Network Topologies

1. Evolution and Enhancement of BitTorrent Network Topologies authors: Cameron Dale, Jiangchuan Liu, Joseph Peters, Bo Li presented by: Cameron Dale Simon Fraser University Burnaby, BC, Canada camerond@cs.sfu.ca IWQoS, June 2nd, 2008, University of Twente, Enschede, The Netherlands

2. Overview Network topology characteristics BitTorrent and its networks Experiments and Results Enhancement and Results Discussion

3. Scale-Free Networks • node degree k has a power law distribution • characterized by: • evolving networks • preferential attachment • tolerant of random node failures

4. Small-World Networks • characteristics of both random and regular networks • large amounts of clustering of nodes • Clustering Coefficient (CC): the fraction of all possible edges that exist between the neighbors of a node • short average distance between nodes • Characteristic Path Length (CPL) • known to be effective for the exchange and dissemination of information

5. Motivation scale-free networks are resilient small-world networks are efficient both are desirable for file sharing, and have been found in other P2P networks recent papers have found clustering in some of the BitTorrent networks all previous results have examined only the initial stage of BitTorrent networks

6. BitTorrent peer-to-peer file sharing program peer connects to a tracker to find other peers tracker returns random list of 50 peers peer connects to other peers number of connections is usually limited default value for most clients is 80

7. BitTorrent Networks • Connection: the neighbors of a peer, to which it is connected by TCP (undirected) • Interest: a peer is interested in other peers that have pieces of the download they need • Unchoked: a peer's indication of a willingness to upload to another peer (incentive mechanism) • Download: a peer is downloading from other peers

8. Related Work Al-Hamra et. al. examined the diameter and found clustering in the Connection network Legout et. al. found clustering in the Unchoked network both used qualitative methods to demonstrate the clustering both examined only the very early stage of a BitTorrent download

9. Basic Experimental Design modified BitTornado client run on more than 400 PlanetLab nodes download a 780 MB sample file node will connect, download, stop, delete, and reconnect multiple times experiments run for over 100 hours

10. Other Experiments • remove the limit on the number of connections a peer can make (was 80) • no effect on most of the results • connectivity matrix is random throughout the experiment • peers join and leave in groups, causing periods of increased and decreased churn • only a small effect on the Clustering Coefficient

11. Distribution of Peer Characteristics

12. Peer Population

13. Scale-Free Analysis plot the degree of each node versus its rank on a log-log scale linear fit gives the power law exponent γ and an R2 goodness of fit value only the Unchoked network had high R2 values of 0.9 (others were less than 0.7) previous real-world experiments show power law exponents between 2 and 3

14. Sample Node Degree Fit

15. Power Law Exponent

16. Small-World Analysis calculate the Characteristic Path Length and Clustering Coefficient of each network at periodic intervals also generate a similar-sized random graph for comparison

17. Characteristic Path Length

18. Characteristic Path Length varies only during the initial stage short due to the density of the graph 440 nodes with average degree 65 very close to the random graph's CPL Unchoked graph is slightly larger may be due to scale-free nature of the graph

19. Clustering Coefficient

20. CC Compared to Random Graph

21. Clustering Coefficient most of the variation is in the initial stage, and none is seen after the transient stage Unchoked network shows some clustering in the initial stage other than that, there is very little clustering, and none after the transient stage lack of clustering means no small-world most networks are essentially random

22. Connectivity Matrix scatter plot of peer connections point at (x, y) indicates that peer x is connected to peer y peers are assigned an index based on the time they joined the system early peers maximize their number of connections quickly used by Al-Hamra et. al. to indicate clustering in the Connection network

23. Connectivity Matrix Comparison Our Results (Hour 4) Al-Hamra et. al.

24. Connectivity Matrix Evolution Hour 4 Hour 16 Hour 8 Hour 32

25. Connectivity Matrix results for initial stage are very similar to previous results later results show increased randomness in the end, the connectivity matrix is completely random the experiment with no limit on the number of connections a peer can make showed a completely random connectivity matrix throughout the experiment

26. Adding Small-World to BitTorrent design a tracker that creates a small-world BitTorrent network of peers only realistic modification possible for BitTorrent try similar strategy to Watts and Strogatz start with a regular graph with a maximized Clustering Coefficient add some randomness

27. A Cycle of n-cliques • connect n-cliques in a cycle • gives a Clustering Coefficient of • independent of the total number of nodes • for BitTorrent, n = 80, so CC = 0.9986 • increases the CPL to twice that of a random graph (3.8)

28. Finding the Small-World add some randomness to the regular graph replace an n-clique edge with a random edge that connects together n-cliques vary the amount of randomness (number of replaced edges) graphs usually become small-world with a randomness of 1% to 3%

29. Adding Randomness to n-cliques

30. Small-World Tracker assign each new peer to an unfilled n-clique if all are full, create a new n-clique choose the list of peers to return: a small fixed number from other n-cliques the rest (the majority) from the peer's n-clique two configuration parameters random-peers: the small fixed number chosen from other n-cliques clique-size: the maximum size of an n-clique

31. Simulation (clique-size = 80)

32. Simulation (N = 400)

33. Small-World Tracker Experiment • experiment is mostly unmodified from the previous one • only change is the Small-World tracker • random-peers: set to 1 • clique-size: set to 40 • same results for peer population, Unchoked network is still scale-free

34. Small-World Tracker CPL

35. Original CC (different scale)

36. Small-World Tracker CC

37. Original CC Compared to Random

38. Small-World Tracker CC to Random

39. Small-World Connectivity Matrix

40. Small-World Tracker Results • small-world tracker has substantially increased the clustering of peers • all networks were affected, though our changes were only to the Connected network • the Characteristic Path Length also increased by 10% to 20% • the BitTorrent peers now form a small-world network

41. Initial Stage Not Typical long-term experiments are needed caused by peer dynamics over time peers become seeds and drop connections to other seeds leaving peers free up connection slots to others new peers are requested periodically from the tracker

42. Scale-Free Unchoked Network power law distribution of nodes with exponent of 2 unaffected by churn and other changes may explain some of BitTorrent's efficiency scale-free graphs are resistant to churn

43. Lack of Clustering almost no clustering in any of the networks almost all graphs are completely random not surprising due to the random peer lists returned by the tracker therefore, no small-world networks are possible

44. Introducing the Small-World theoretical framework for creating dense small-world graphs simple implementation in a BitTorrent tracker dramatically increases the clustering of peers all networks show small-world properties

45. Future Work experimentally determine the efficiency of small-world BitTorrent networks improvements to the Small-World Tracker: consider effect of small n-cliques on peers what happens when many peers leave (consolidating n-cliques) further examination of the effect of churn on the clustering of BitTorrent peers

46. Evolution and Enhancement of BitTorrent Network Topologies authors: Cameron Dale, Jiangchuan Liu, Joseph Peters, Bo Li presented by: Cameron Dale Simon Fraser University Burnaby, BC, Canada camerond@cs.sfu.ca IWQoS, June 2nd, 2008, University of Twente, Enschede, The Netherlands