QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Service Selection Algorithms for Web Services with End-to-End QoS Constraints
CEC '04 Proceedings of the IEEE International Conference on E-Commerce Technology
Efficient algorithms for Web services selection with end-to-end QoS constraints
ACM Transactions on the Web (TWEB)
Adaptive Service Composition in Flexible Processes
IEEE Transactions on Software Engineering
Genetic algorithm-based optimization of service composition and deployment
Proceedings of the 3rd international workshop on Services integration in pervasive environments
A framework for QoS-aware binding and re-binding of composite web services
Journal of Systems and Software
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
Combining global optimization with local selection for efficient QoS-aware service composition
Proceedings of the 18th international conference on World wide web
A Heuristic QoS-Aware Service Selection Approach to Web Service Composition
ICIS '09 Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science
QoS-Aware composition of web services: an evaluation of selection algorithms
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
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Recently, a lot of research has been dedicated to optimizing the QoS-aware service composition. This aims at selecting the optimal composed service from all possible service combinations regarding user's end-to-end quality requirements. Existing solutions often employ the global optimization approach, which does not show promising performance. Also, the complexity of such methods extensively depends on the number of available web-services, which continuously increase along with the growth of the Internet. Besides, the local optimization approaches have been rarely utilized, since they may violate the global constraints. In this paper, we propose a top-down structure, named quality constraints decomposition (QCD) here, to decompose the global constraints into the local constraints, using the genetic algorithm (GA). Then the best web service for each task is selected through a simple linear search. In contrast to existing methods, the QCD approach mainly depends on a limited set of tasks, which is considerably less complex, especially in the case of dynamically distributed service composition. Experimental results, based on a well-known data set of web services (QWSs), show the advantages of the QCD method in terms of computation time, considering the number of web services.