Probabilistic Qualitative Preference Matching in Long-Term IaaS Composition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10601)


We 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.



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.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia
  2. 2.School of ScienceRMIT UniversityMelbourneAustralia
  3. 3.Department of Computer Science and EngineeringQatar UniversityDohaQatar

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