Building small worlds in unstructured P2P networks using a multi-agent Bayesian inference mechanism

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Over the past few years, peer-to-peer(p2p) unstructured networks have emerged as an attractive paradigm for enabling online interactions between a large number of users in a decentralized manner. However, the decentralized nature of unstructured p2p networks makes load balancing a challenging problem. Specifically, the self-interested nature of users on the nodes of a p2p network and dynamic changes in network topology give rise to an unbalanced distribution of nodes across an unstructured p2p network. This results in network congestion and significant search latencies for all nodes. In this paper, we describe a small-world network model and a Bayesian inference mechanism within a multiagent setting to address these issues. Simulation results for a file sharing p2p application show that our algorithm achieves an exponential reduction in number of messages exchanged and improves load-balancing across the network.

Original languageEnglish (US)
Title of host publicationAAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems
Pages940-942
Number of pages3
DOIs
StatePublished - Dec 1 2007
Event6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07 - Honolulu, HI, United States
Duration: May 14 2008May 18 2008

Publication series

NameProceedings of the International Conference on Autonomous Agents

Conference

Conference6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07
CountryUnited States
CityHonolulu, HI
Period5/14/085/18/08

Fingerprint

Small World
P2P Network
Bayesian inference
Resource allocation
Small-world networks
Load Balancing
Decentralized
Vertex of a graph
Small-world Network
Peer-to-peer (P2P)
Topology
Congestion
Network Topology
Network Model
Latency
Sharing
Paradigm
Interaction
Simulation

Keywords

  • P2P networks
  • Small world graphs
  • Topology balancing

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Theoretical Computer Science

Cite this

Dasgupta, P. (2007). Building small worlds in unstructured P2P networks using a multi-agent Bayesian inference mechanism. In AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 940-942). [144] (Proceedings of the International Conference on Autonomous Agents). https://doi.org/10.1145/1329125.1329300

Building small worlds in unstructured P2P networks using a multi-agent Bayesian inference mechanism. / Dasgupta, Prithviraj.

AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. 2007. p. 940-942 144 (Proceedings of the International Conference on Autonomous Agents).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Dasgupta, P 2007, Building small worlds in unstructured P2P networks using a multi-agent Bayesian inference mechanism. in AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems., 144, Proceedings of the International Conference on Autonomous Agents, pp. 940-942, 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS'07, Honolulu, HI, United States, 5/14/08. https://doi.org/10.1145/1329125.1329300
Dasgupta P. Building small worlds in unstructured P2P networks using a multi-agent Bayesian inference mechanism. In AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. 2007. p. 940-942. 144. (Proceedings of the International Conference on Autonomous Agents). https://doi.org/10.1145/1329125.1329300
Dasgupta, Prithviraj. / Building small worlds in unstructured P2P networks using a multi-agent Bayesian inference mechanism. AAMAS'07 - Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. 2007. pp. 940-942 (Proceedings of the International Conference on Autonomous Agents).
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