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AuthorMistry, Sajib
AuthorBouguettaya, Athman
AuthorDong, Hai
AuthorErradi, Abdelkarim
Available date2023-04-10T09:10:06Z
Publication Date2017
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
URIhttp://dx.doi.org/10.1007/978-3-319-69035-3_18
URIhttp://hdl.handle.net/10576/41827
AbstractWe propose a qualitative similarity measure approach to select an optimal set of probabilistic Infrastructure-as-a-Service (IaaS) requests according to the provider's probabilistic preferences over a long-term period. The long-term qualitative preferences are represented in probabilistic temporal CP-Nets. The preferences are indexed in a k-d tree to enable the multidimensional similarity measure using tree matching approaches. A probabilistic range sampling approach is proposed to reduce the large multidimensional search space in temporal CP-Nets. A probability distribution matching approach is proposed to reduce the approximation error in the similarity measure. Experimental results prove the feasibility of the proposed approach. Springer International Publishing AG 2017.
SponsorAcknowledgements. This research was made possible by NPRP 7-481-1-088 grant from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherSpringer Verlag
SubjectInfrastructure-as-a-Service (IaaS)
qualitative similarity measure approach
K-d tree
CP-nets
TitleProbabilistic qualitative preference matching in long-term IaaS composition
TypeConference Paper
Pagination256-271
Volume Number10601 LNCS
dc.accessType Abstract Only


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