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dc.contributor.authorShen, Xiang-Jun
dc.contributor.authorChang, Qing
dc.contributor.authorLiu, Lu
dc.contributor.authorPanneerselvam, John
dc.contributor.authorZha, Zheng-Jun
dc.date.accessioned2016-11-15T12:08:40Z
dc.date.available2016-11-15T12:08:40Z
dc.date.issued2016-06-01
dc.identifier.citationShen, X. et al (2016) 'CCLBR: Congestion Control-Based Load Balanced Routing in Unstructured P2P Systems, IEEE Systems Journal, PP (99)en
dc.identifier.issn1932-8184
dc.identifier.doi10.1109/JSYST.2016.2558515
dc.identifier.urihttp://hdl.handle.net/10545/620853
dc.description.abstractGiven the growing popularity of the peer-to-peer (P2P) network systems in the recent years, efficient query routing under highly dynamic environments is still lacking in several P2P network systems. In response to this challenge, this paper proposes a new churn-resilient system to find alternative routing paths for the purpose of balancing the query loads under higher network churns and heavy workloads, ultimately to improve the search efficiency. Two novel methods are devised to balance the network query loads among both inter- and intragroup level peers. First, a resource grouping and a rewiring method is proposed to spontaneously organize and cluster the peers having same resources together. This strategy facilitates the peers to evolve the network into a cluster-like topology and balances the query loads among the intergroup peers. Second, a collaborative Q-learning method is proposed to balance the query loads among the intragroup peers in order to intelligently avoid queries being forwarded to the congested peers in the network. Experiments conducted under dynamic network scenarios demonstrate that our proposed method achieves better search performances with a more balanced network load than the existing methods, and further exhibits higher robustness and adaptability under higher network churns and heavy network loads.
dc.language.isoenen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7482746/en
dc.rightsArchived with thanks to IEEE Systems Journalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectCollaborative Q-learningen
dc.subjectCongestion controlen
dc.subjectLoad balancingen
dc.subjectQuery routingen
dc.titleCCLBR: Congestion control-based load balanced routing in unstructured P2P systemsen
dc.typeArticleen
dc.identifier.eissn1937-9234
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalIEEE Systems Journalen
dcterms.dateAccepted2016-04-16
html.description.abstractGiven the growing popularity of the peer-to-peer (P2P) network systems in the recent years, efficient query routing under highly dynamic environments is still lacking in several P2P network systems. In response to this challenge, this paper proposes a new churn-resilient system to find alternative routing paths for the purpose of balancing the query loads under higher network churns and heavy workloads, ultimately to improve the search efficiency. Two novel methods are devised to balance the network query loads among both inter- and intragroup level peers. First, a resource grouping and a rewiring method is proposed to spontaneously organize and cluster the peers having same resources together. This strategy facilitates the peers to evolve the network into a cluster-like topology and balances the query loads among the intergroup peers. Second, a collaborative Q-learning method is proposed to balance the query loads among the intragroup peers in order to intelligently avoid queries being forwarded to the congested peers in the network. Experiments conducted under dynamic network scenarios demonstrate that our proposed method achieves better search performances with a more balanced network load than the existing methods, and further exhibits higher robustness and adaptability under higher network churns and heavy network loads.


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