The service composer has to ensure that the

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end-to-end QoS requirements (like bandwidth, latency and price). The service composer has to ensure that the aggregated QoS values of the selected services match the user requirements at the start of the execution as well as during the execution. However, dynamic changes due to changes in the QoS requirements (e.g. the user switched to a network with lower bandwidth) or failure of some services (e.g. some of the selected services become unavailable) can occur at run-time. Therefore, a quick response to adaptation requests is important in such applications. Exponential time complexity for an infrastructure service like the composition service is only acceptable if the number of service candidates and constraints are very limited. Already in larger enterprises and even more in open service infrastructures with a few thousands of services the response time for a service composition request could already be out of the real-time requirements. Fig. 1. Composition of Multimedia Web Services Two general approaches exist for the QoS-aware service selection: local selection and global selection. In local selection, one service is selected from each class indepen- dently. Using a given utility function, each service candidate is assigned a utility value and the service with maximum utility value is selected. This approach is very efficient in terms of computation time as the time complexity of the local optimization approach is O ( l ) , where l is the number of service candidates in each class. This is specially useful for distributed environments where central QoS management is not desirable. However, local selection is not suitable for QoS-based service composition, with global end-to- end requirements (like maximum total price), since such global constraints cannot be verified locally. On the other hand, the global selection [2–5] aims at solving the problem on the composite service level by considering all possible service combinations. The aggre- gated QoS values of each service combination is computed. This approach seeks the service combination, which maximizes the aggregated utility value, while guarantee- ing global constraints. The global selection problem can be modeled as a Multi-Choice Multidimensional Knapsack problem (MMKP), which is known to be NP-hard in the strong sense [6]. Therefore it can be expected that an optimal solution may not be found in a reasonable amount of time [7]. Since the business requirements (such as response times, throughput or availabil- ity) are only approximate, we argue that finding a reasonable selection of services that covers the requirements ”‘approximately”’ and avoids obvious violations of constraints at acceptable costs is more important than finding ”‘the optimal”’ selection of services with a very high cost. The aim of our study is to find a compromise between optimality
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3 and performance by combining local selection and global optimization to benefit from the advantages of both techniques.
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