Evolutionary Algorithm with AHP Decision-Making Method for Cloud Workflow Service Composition

Multiple Cloud services can be composed as a single service to fulfill the execution of large-scale workflow application. It faces trade-offs among several QoS metrics causing by users' preferences. Most existing works utilize multi-objective evolutionary algorithms to address this problem. However, it is hardly to express different users' preferences. This paper proposes an evolutionary algorithm for Cloud service composition, combining NSGA-II with Decision-Making method to calculate Crowding Distance with triangular fuzzy number based on AHP. The experimental results show that our approach could precisely capture users' preferences, and perform better in scalability than other general multi-objective algorithms.

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