Unstructured P2P Networks: Topological Properties and Search Performance

1. Unstructured P2P Networks: Topological Properties and Search Performance George Fletcher, Hardik Sheth and Katy Börner Indiana University, Bloomington, USA AP2PC 2004 19 July 2004

2. Outline • Motivation & Methodology • P2P Network Models • Topological Measurements • Unstructured P2P Search Algorithms • Search Performance

3. Motivation & Methodology • Topological Properties of Proposed Unstructured P2P Networks • Understand Search Performance on These Models • Experimental Set Up

4. P2P Network Models • Structured (Distributed Hash Tables) • CAN (Ratnasamy et al 2001) • Chord (Stoica et al 2003) • Unstructured • Hypergrid (Saffre and Ghanea-Hercock 2003) • PRU (Pandurangan, Raghavan, and Upfal 2003)

5. Structured P2P Models • CAN and Chord

6. Unstructured P2P Models • Random

7. Unstructured P2P Models • PRU and Hypergrid

8. InfoVis CyberInfrastructure • School of Library and Information Science at Indiana University • http://iv.slis.indiana.edu • http:// ivc.sourceforge.net • Demo

9. Topological Properties

10. Degree FrequencyDistribution • CAN and Random • PRU • Hypergrid

11. Search Algorithms • What is the best we can do for decentralized search in a small-world? • Definition of Search and Search Cost • Uninformed Search • p-Random BFS • k-Random Walk • Informed Search • Generic Adaptive Probabilistic Search (GAPS)

12. Structured vs. Unstructured

13. BFS andRandom Walk • 1-Random Walk • 0.0 and 0.5 BFS

14. 8 and 16-Random Walk

15. Response Time

16. GAPS, 5% popular, 75% weight

17. GAPS Improvement Over Time

18. Search Performance • Hypergrid • PRU • Random

19. Conclusions • Unstructured models generate small worlds • Hard to beat “flooding” on a random graph • GAPS is a good candidate for further investigation

20. Future Directions • Extensive formal analysis of GAPS • Investigate adaptive topologies for GAPS

21. Thanks! • http://iv.slis.indiana.edu • http://ivc.sourceforge.net