1. By: Sraddha Adhikari St107931 TC/SET Thesis Examination Committee: Dr. Teerapat Sanguankotchakorn (Chairperson) Assoc. Prof. Tapio J. Erke Dr. Poompat Saengudomlert Prof. Noel Crespi (External Expert) Dr. Mehdi Mani (External Expert) Asian Institute of Technology May 17, 2010

2. Why P2P? Bandwidth Bottleneck has shifted from Users Side (e.g. dial up connection) to Central Server Development of popular sites like YouTube hindered by client/server architecture [4] src: [11] 2

3. P2P versus C/S 3

4. Peer-to-Peer Networks Peers or nodes are the basic building blocks Shift of load and responsibility to all entities in P2P network Peers incorporate with each other to accomplish some task/objectives Resources in the network scale with the number of peers in the system Reliable network: no single point of failure Resilient Network: avoid dependence on central resources 4

5. PROBLEM STATEMENT P2P networks (overlay network) have their own routing mechanisms that play major role in their performance [9]. Content Discovery is still identified as a major problem in Unstructured P2P networks [1] [2] [6] [11] [12]. Nodes in P2P networks do not have global view of the network which leads to inefficient routing of query. P2P delivery of short videos demands an efficient content location mechanism [4]. 5

6. Content Discovery Problem The content discovery problem is simple to state: Given a data item X stored at some dynamic set of nodes in the system, find it. A G F E D C B I want X I HAVE X

7. Various Search Mechanisms: Flooding Most typical query method in Decentralized and Unstructured P2P networks Query Flooding overloads the whole network with redundant messages Difficult to choose appropriate TTL to terminate the flood. Many duplicate messages introduced by flooding Gnutella, first decentralized P2P application has performance issues like generation of huge network traffic, slow response and congestion [5] [12] 7

8. Various Search Mechanisms: Expanding Ring Introduced to improve search in Gnutella by [7]. Value of TTL is gradually increased to find the content There is duplication of messages to the same peers Peers do not learn from past experiences to bypass previously forwarded peers This approach still floods the network with messages 8

9. Various Search Mechanisms: Random Walk Totally blind search where nodes forward their query to a random node at each hop If the forwarded node is overloaded with queries, it would take time to process, which adds to delay of random walk mechanism [3]. Sequential search, hence search time increases 9

10. Peer-to-peer & Social Networks Due to the fact that there is human being behind every peer there is similarity between social networks and peer-to-peer networks. 10

11. Proposal: Social P2P Network 11 We propose to implement human strategies in social networks to improve search mechanism in P2P. In short, we propose a social P2P network (socP2P). We believe and will verify that human strategies in social networks are useful in improving content discovery in P2P network.

12. Objectives of Proposed Search Mechanism Locate resources in the network efficiently with - high success rate - low overhead - low delay Detect nodes having similar interest and use them to make search more effective. Exploit “search mechanism” to gather interest information of nodes in network so that additional overhead is not required to obtain these information. Develop an efficient and scalable “Resource Discovery Algorithm” in P2P networks using social relationships between peers. Evaluate the proposed algorithm by simulation and comparisons. 12

13. Limitations We do not consider any dynamics due to node joins and leaves and content sharing and removing. We do not consider issues of selfish peers who do not contribute their resources to the network and are commonly known as free-riders. 13

14. Human Characteristics copied in proposed algorithm Human beings can be grouped into different interest categories. We find out resources that we are looking for by directly contacting some acquaintances that have knowledge about the resource that we are looking for. When we interact with people, we try to remember useful information (what they are involved in, save their contact number) so that we can use these information later. 14

15. Detection of Interest Similarity in socP2P Nodes do not declare their interest. Interest of nodes is learned during the search process. If ‘B’ replies successfully to the query of ‘A’, it is learnt that they have interest on the same file. Hence Interest Similarity between then is declared. A directed Interest Link is created from A to B. Considering the interest similarity between them, B might be useful to A in future as well. 15

16. Two Principals of Our Approach Principal 1 Use search queries to automatically adapt to make resource related interest relationships. Principal 2 Exploit the query request encounters by nodes to keep a record of what the requesting node is interested in? - It is overheard knowledge by node - Helps in recommending “right nodes” to the querying node. 16

17. Two Types of Links created Based on Interest Similarity Directed Interest Links Network Suggested Forward and thus Created Links 17

18. An Example of Directed Interest Link and Recommendation B D C A E Find X A wants X Find X D A B C C gives X to A

19. Search Restricted to ‘M’ Nodes Search is limited to two hops. Our goal is to restrict number of Queries submitted to the network for a single content. Query Node sends its query to M/2 nodes (neighbors or friends or both) in the first hop. If these M/2 Nodes do not have requested content, each of them forward the query to only one node. Thus query is forwarded to maximum (1 x (M/2) + (M/2) x 1) = M 19

20. Search Mechanism in socP2P 20

21. Recommended Nodes Based Search 21

22. Friend Based Search 22

23. SIMULATION & RESULTS MATLAB CODE Each result is simulated 20 times except for Network Size 1000 (12-15 times). Simulation Methodology - Network Generation - Content Generation & Distribution - Query Generation 23

24. Average Success Rate 24 In the above formula, NBS = Neighbors Based Search FBS = Friends Based Search RBS = Recommended Nodes Based Search

25. Average Success Rate (ASR) 25 Average Success Rate (%) Percentage of nodes a query is forwarded to

26. Average Success Rate (ASR) 26 Average Success Rate (%) Percentage of nodes a query is forwarded to

27. Average Success Rate (ASR) 27 Average Success Rate (%) Percentage of nodes a query is forwarded to

28. Average Success Rate (ASR) 28 Average Success Rate (%) Percentage of nodes a query is forwarded to

29. Average Success Rate (ASR) 29 Average Success Rate (%) Percentage of nodes a query is forwarded to

30. ASR in relation to Number of Queries 30 Average Success Rate (%) Percentage of nodes a query is forwarded to

31. Comparison between socP2P, socP2P without Node Overhearing & Random Walk 31 Average Success Rate (%) Percentage of nodes a query is forwarded to

32. Comparison between socP2P, socP2P without Node Overhearing & Random Walk 32 Average Success Rate (%) Percentage of nodes a query is forwarded to

33. Comparison between socP2P, socP2P without Node Overhearing & Random Walk 33 Average Success Rate (%) Percentage of nodes a query is forwarded to

34. Average Success Rate versus Popularity of Files 34 Average Success Rate (%) Popularity of resource

35. Average Success Rate versus Popularity of Files 35 Popularity of resource Average Success Rate (%)

36. Average Success Rate versus Popularity of Files 36 Popularity of resource Average Success Rate (%)

37. Average Success Rate versus Popularity of Files 37 Average Success Rate (%) Popularity of resource

38. Shortest Path to Content 38 Shortest Path to Content (Overlay hops) Percentage of nodes a query is forwarded to

39. Shortest Path to Content (Comparison socP2P & Random Walk) 39 Shortest Path to Content (Overlay hops) Percentage of nodes a query is forwarded to

40. Shortest Path to Content (Comparison with Gnutella, Gnutella with Shortcuts and socP2P) 40 Shortest Path to Content (Overlay hops)

41. Network Size and Network value 41 Average Success Rate (%)

42. Network Size and Network Value 42 Average Success Rate (%)

43. Code Validation ParametersValue Total Request884 Documents609 Total Number of Peers735 43 Table: Simulation Parameters Used By Reference Paper Observation Parameter: Average Success Rate

44. Performance similarity 44 Average Success Rate (%) Simulation Length (minutes)

45. Conclusion Friend lists of nodes, overheard knowledge by nodes and recommendation by nodes on the basis of overheard knowledge help to improve search in P2P network. socP2P has achieved high success rate compared to random walk and Gnutella with shortcuts. High success rate is achieved with less delay and low overhead in the network. 45

46. Future Work Distribution of content taking care of bandwidth efficiency, delay and load distribution in the network. Possible works directed towards enhancing socP2P: Mutual Interest Links Identifying rare files to increase their success rate Including Overhearing Part in the Forwarded Node 46

47. References [1] Balakrishnan, H., Kaashoek, M.F., Karger, D., Morris, R. & Stoica, I. (2003). Looking Up Data in P2P Syatems. Communications of the ACM, 46(2), 43-48. [2] Bisnik, N. & Abouzeid, A. (2005). Modelling and Analysis of Random Walk Search Algorithms in P2P Networks. Hot-p2p, Second International Workshop on Hot Topics in Peer-to-Peer Systems, pp.95-103. [3] Chawathe, Y., Ratnasamy, S., Breslau, L., Lanham, N. & Shenker, S. (2003). Making Gnutella-like P2P Systems Scalable. Making Gnutella-like P2P Systems Scalable. Proceedings of ACM SIGCOMM, Kasruhe, Germany, 2003. [4] Cheng, X. & Liu, J. (2009). NetTube: Exploring Social Networks for Peer-to-Peer Short Video Sharing. IEEE INFOCUM 2009 Proceedings, 2009, 1152-1160. [5] Hui, K.Y.K., Lui, J.C.S., & Yau, D.K.Y. (2006). Small World Overlay P2P Networks. Computer Networks, vol. 50, no. 15, pp.2727-2746, 2006. [6] Liu, L., Antonopoulos, N., & Mackin, S. (2007). Social Peer-to-Peer for Resource Discovery. 15 th EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing (PDP’07), Page(s): 459-466. [7] Lv, Q., Cao, P., Cohen, E., Li, K., & Shenker, S. (2002). Search and replication in unstructured peer-to-peer networks. Proceedings of the 16th international conference on Supercomputing, New York, USA, 84-95. 47

48. References [8] McGarthwaite, L. (2005). Client-Server versus Peer-to-Peer Architecture: Comparisons for Streaming Video. Proceedings of the 5 th Winona Computer Science, Undergraduate Research Seminar, April 20-21, 2005, Winona, MN, US. [9] Ripeanu, M. (2001). Peer-to-Peer Architecture Case Study: Gnutella Network. Proceedings of IEEE 1 st International Conference on Peer-to-peer Computing, Linkoping Sweden. [10] Sripanidkulchai, K., Maggs, B., & Zhang, H. (2003). Efficient Content Location Using Interest-Based Locality in Peer-to-Peer Systems. INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, Volume: 3, Page(s): 2166-2176 vol.3. [11] Tang, J., Zhang, W., Xiao, W., Tang, D., & Song, J. (2006). Self-Organizing Service- Oriented Peer Communities. [12] Upadrashta, Y., Vassileva, J. & Grassmann, W. (2005). Social Networks in Peer-to- Peer Systems. [13] url: http://www.cs.virginia.edu/~mngroup/hypercast/general.html 48

49. Thank you ! Questions and Suggestions are welcome !! 49