document: Combining global optimization with local selection for efficient QoS-aware service composition The run-time binding of web services has been recently put forward in order to support rapid and dynamic web service compositions . With the growing number of alternative web services that provide the same functionality but differ in quality parameters , the service composition becomes a decision problem on which component services should be selected such that user 's end-to-end QoS requirements ( e.g. availability , response time ) and preferences ( e.g. price ) are satisfied . Although very efficient , local selection strategy fails short in handling global QoS requirements . Solutions based on global optimization , on the other hand , can handle global constraints , but their poor performance renders them inappropriate for applications with dynamic and real-time requirements . In this paper we address this problem and propose a solution that combines global optimization with local selection techniques to benefit from the advantages of both worlds . The proposed solution consists of two steps : first , we use mixed integer programming ( MIP ) to find the optimal decomposition of global QoS constraints into local constraints . Second , we use distributed local selection to find the best web services that satisfy these local constraints . The results of experimental evaluation indicate that our approach significantly outperforms existing solutions in terms of computation time while achieving close-to-optimal results .
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