Adaptive Probabilistic Search (APS) for Peer-to-Peer Networks

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dc.contributor.authorTsoumakos, Dimitriosen_US
dc.contributor.authorRoussopoulos, Nicken_US
dc.date.accessioned2004-05-31T23:26:04Z
dc.date.available2004-05-31T23:26:04Z
dc.date.created2003-02en_US
dc.date.issued2003-02-27en_US
dc.description.abstractPeer-to-Peer networks are gaining increasing attention from both the scientific and the large Internet user community. Popular applications utilizing this new technology offer many attractive features to a growing number of users. At the heart of such networks lies the data retrieval algorithm. Proposed methods either depend on the network-disastrous flooding and its variations or utilize various indices too expensive to maintain. In this report we describe an adaptive, bandwidth-efficient and easy to deploy search algorithm for unstructured Peer-to-Peer networks, the Adaptive Probabilistic Search method (APS). Our scheme utilizes feedback from previous searches to probabilistically guide future ones. Extensive simulation results show that APS achieves high success rates, increased number of discovered objects, very low bandwidth consumption and adaptation to changing topologies. UMIACS-TR-2003-21en_US
dc.format.extent255068 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/1263
dc.language.isoen_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4451en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2003-21en_US
dc.titleAdaptive Probabilistic Search (APS) for Peer-to-Peer Networksen_US
dc.typeTechnical Reporten_US

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