Towards mashup optimisation with global constraints in the cloud. (1st January 2014) - Yang, Wanchun; Zhang, Chenxi; Mu, Bin
With the growing number of services being deployed in cloud environment, it becomes difficult to facilitate mashup quickly. In this paper, we introduce a comprehensive evaluation model of mashup which considers three aspects, including functional similarity, QoS characteristics and transactional properties, and use them together as optimisation criteria. To enlarge the scope of feasible solutions, we adopt Analytic Hierarchy Process (AHP) to analyse the global constraints and consider relevant services with different granularity. An efficient mashup optimisation algorithm based on skyline and hybrid particle swarm optimisation is proposed to seek a near-to-optimal solution. To overcome the premature convergence of traditional particle swarm optimisation, a series of effective strategies are presented, which include global mutation operation and adaptive inertia weight parameter. The experimental results show that our proposed approach is feasible and effective.