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Multiobjective Optimization for Brokering of Multicloud Service Composition

Published:15 April 2016Publication History
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Abstract

The choice of cloud providers whose offers best fit the requirements of a particular application is a complex issue due to the heterogeneity of the services in terms of resources, costs, technology, and service levels that providers ensure. This article investigates the effectiveness of multiobjective genetic algorithms to resolve a multicloud brokering problem. Experimental results provide clear evidence about how such a solution improves the choice made manually by users returning in real time optimal alternatives. It also investigates how the optimality depends on different genetic algorithms and parameters, problem type, and time constraints.

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        • Published in

          cover image ACM Transactions on Internet Technology
          ACM Transactions on Internet Technology  Volume 16, Issue 2
          April 2016
          150 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/2909066
          • Editor:
          • Munindar P. Singh
          Issue’s Table of Contents

          Copyright © 2016 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 15 April 2016
          • Revised: 1 December 2015
          • Accepted: 1 December 2015
          • Received: 1 July 2015
          Published in toit Volume 16, Issue 2

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