Genetic algorithm based QoS-aware service compositions in cloud computing
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We present an optimization approach for service compositions in large-scale service-oriented systems that are subject to Quality of Service (QoS) constraints. In particular, we leverage a composition model that allows a flexible specification of QoS constraints by using constraint hierarchies. We propose an extensible met heuristic framework for optimizing such compositions. It provides coherent implementation of common met heuristic functionalities, such as the objective function, improved mutation or neighbor generation. We implement three met heuristic algorithms that leverage these improved operations. The experiments show the efficiency of these implementations and the improved convergence behavior compared to purely randomized met heuristic operators.