Using genetic algorithms to reorganize superpeer structure in peer to peer networks

Authors:
Jaymin Kessler;Khaled Rasheed;I. Budak Arpinar
Affiliations:
Artificial Intellignece Center, University of Georgia, Athens, USA;Computer Science Department, University of Georgia, Athens, USA;Large Scale Distributed Information Systems LAB (LSDIS), Computer Science Department, University of Georgia, Athens, USA
Venue:
Applied Intelligence
Year:
2007

Citing 0
Cited 3

A Framework for Evolutionary Peer-to-Peer Overlay Schemes

EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Multi-objective peer-to-peer neighbor-selection strategy using genetic algorithm

HiPC'07 Proceedings of the 14th international conference on High performance computing
Handwritten characters recognition based on nature-inspired computing and neuro-evolution

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this article, we describe a genetic algorithm for optimizing the superpeer structure of semantic peer to peer networks. Peer to peer, also called P2P, networks enable us to search for content or information in a distributed fashion across a large number of peers while providing a level of fault tolerance by preventing disconnecting peers from disrupting the network. We seek to maximize the number of queries answered while minimizing the time in which they are answered. It will be shown that the genetic algorithm (GA) dramatically improves network performance and consistently finds networks better than those found by random search and hill climbing. A comparison will also be made to networks found through exhaustive search, showing that the GA will, for smaller networks, converge on a globally optimal solution.