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Gossip‐based search selection in hybrid peer‐to‐peer networks
Author(s) -
Zaharia M.,
Keshav S.
Publication year - 2008
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1188
Subject(s) - computer science , gossip , peer to peer , flooding (psychology) , distributed hash table , popularity , bandwidth (computing) , selection (genetic algorithm) , distributed computing , computer network , machine learning , psychology , social psychology , psychotherapist
We present GAB, a search algorithm for hybrid peer‐to‐peer networks, that is, networks that search using both flooding and a distributed hash table (DHT). GAB uses a gossip‐style algorithm to collect global statistics about document popularity to allow each peer to make intelligent decisions about which search style to use for a given query. Moreover, GAB automatically adapts to changes in the operating environment. Synthetic and trace‐driven simulations show that compared to a simple hybrid approach that always floods first, trying a DHT if too few results are found, GAB reduces the response time by 25–50% and the average query bandwidth cost by 45%, with no loss in recall. GAB scales well, with only a 7% degradation in performance despite a tripling in system size. Copyright © 2007 John Wiley & Sons, Ltd.