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dc.contributor.authorShen, H
dc.contributor.authorLi, K
dc.contributor.authorPan, Y
dc.contributor.authorYoung, GH
dc.contributor.authorZheng, SQ
dc.date.accessioned2020-01-03T04:12:44Z
dc.date.available2020-01-03T04:12:44Z
dc.date.issued1998
dc.identifier.issn0743-7315
dc.identifier.doi10.1006/jpdc.1998.1440
dc.identifier.urihttp://hdl.handle.net/10072/122186
dc.description.abstractWe study the problem of dynamic tree embedding ink-partite networks Gk and analyze the performance on interpartition load distribution of the embedding. We show that, for ring-connected Gk, if the embedding proceeds by taking a unidirectional random walk at a length randomly chosen from [0, Δ − 1], where Δ is a multiple of k, the best-case performance is achievable at probability 2ke−k, which is much higher than the asymptotically zero probability at which the worst-case performance may appear. We also show that the same probabilities hold for fully connected Gk if the embedding proceeds by taking a random walk at a length randomly chosen from [2, ∞). When k= 2 (bipartite networks), our results show that if we do the embedding under the above random-walk schemes in their corresponding networks, we will have a 50% chance to achieve the best-case performance. We also analyze the performances for embedding in these networks in the expected case and observe the interesting fact that they match the performances in the best case when the network isk-partitionable into partitions of equal size.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAcademic Press
dc.publisher.placeUSA
dc.relation.ispartofpagefrom144
dc.relation.ispartofpageto156
dc.relation.ispartofissue1-2
dc.relation.ispartofjournalJournal of Parallel and Distributed Computing
dc.relation.ispartofvolume50
dc.subject.fieldofresearchComputer Software
dc.subject.fieldofresearchDistributed Computing
dc.subject.fieldofresearchcode0803
dc.subject.fieldofresearchcode0805
dc.titlePerformance Analysis for Dynamic Tree Embedding in K-partite Networks by Random Walk
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorShen, Hong


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