With the growing application of services on the basis of physical networks and Internet of Things devices, service composition based on quality of service (QoS) has attracted wide attention. However, QoS-aware service composition with multiple QoS constraints is time consuming work. Given the evident user preference for QoS, we propose a service composition framework and a QoS-aware method incorporating local and global optimization models. Candidate services are preprocessed and assigned different priority levels based on local optimization. The priority weight of quality attributes is obtained by a fuzzy analytic hierarchy process and the approximate optimal composite service is derived by global optimization for multiple QoS constraints. Simulations verify the proposed method outperforms traditional integer programming (IP) and genetic algorithm, particularly for large scale datasets, providing near optimal solutions with significantly less time cost.
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