Integer and combinatorial optimization
Integer and combinatorial optimization
Quality driven web services composition
WWW '03 Proceedings of the 12th international conference on World Wide Web
Computational Techniques of the Simplex Method
Computational Techniques of the Simplex Method
QoS computation and policing in dynamic web service selection
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
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
Investigating web services on the world wide web
Proceedings of the 17th international conference on World Wide Web
Q-Peer: A Decentralized QoS Registry Architecture for Web Services
ICSOC '07 Proceedings of the 5th international conference on Service-Oriented Computing
A negotiation based approach for service composition
DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
Establishing composite SLAs through concurrent QoS negotiation with surplus redistribution
Concurrency and Computation: Practice & Experience
A novel service selection based on resource-directive decomposition
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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QoS-based service selection aims at finding the best component services that satisfy the end-to-end quality requirements. The problem can be modeled as a multi-dimension multi-choice 0-1 knapsack problem, which is known as NP-hard. Recently published solutions propose using linear programming techniques to solve the problem. However, the poor scalability of linear program solving methods restricts their applicability to small-size problems and renders them inappropriate for dynamic applications with run-time requirements. In this paper, we address this problem and propose a scalable QoS computation approach based on a heuristic algorithm, which decomposes the optimization problem into small sub-problems that can be solved more efficiently than the original problem. Experimental evaluations show that near-to-optimal solutions can be found using our algorithm much faster than using linear programming methods.