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http://dx.doi.org/10.3837/tiis.2015.09.002

Multi-objective Optimization Model with AHP Decision-making for Cloud Service Composition  

Liu, Li (School of Automation and Electrical Engineering, University of Science and Technology)
Zhang, Miao (School of Automation and Electrical Engineering, University of Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.9, no.9, 2015 , pp. 3293-3311 More about this Journal
Abstract
Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.
Keywords
Multi-Cloud; Services Composition; Multi-objective Evolutionary Algorithm; AHP;
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