QoS-Aware Peer Services Selection Using Ant Colony Optimisation
Web services coordinated by computational peers can be aggregated to create composite workflows that provide streamlined functionality for human users or other systems. One of the most critical challenges introduced by Peer-to-Peer (P2P) based Web services is represented by Quality of Service (QoS)-driven services composition. Since many available Peers provide overlapping or identical functionalities, though with different QoS, selections need to be quickly made to determine which peers are suitable to participate in an expected composite service. The main contribution of this paper is a heuristic approach which effectively and adaptively finds appropriate service peers for a service workflow composition, and also some uncertainties in the real ad-hoc scenarios are considered by a proper re-planning scheme. We propose to adopt Ant Colony Optimisation (ACO) to tackle the QoS-aware Peers’ composition problem in both static and dynamic situations, as ACO represents a more scalable choice, and is suitable to handle and balance generic QoS attributes by pheromones. The proposed approach is able to improve the selection performances in various service composition structures, and also can adaptively handle unexpected events. We present experimental results to illustrate the efficiency and feasibility of the proposed method.
KeywordsP2P QoS ACO service selection composition
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- 2.Canfora, G., Penta, M.D., Esposito, R., Villani, M.L.: An approach for QoS-aware service composition based on genetic algorithms. In: Proceedings of the 2005 conference on Genetic and evolutionary computation, New York, USA, pp. 1069–1075 (2005)Google Scholar
- 3.Cao, L., Li, M., Cao, J.: Using genetic algorithm to implement cost-driven Web service selection. Multiagent and Grid Systems 3(1), 9–17 (2007)Google Scholar
- 5.Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimisation by Ant Colonies. In: Proceedings of the European Conference on Artificial Life, Paris, France, pp. 134–142. Elsevier Publishing, Amsterdam (1991)Google Scholar
- 7.Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man and Cybernetics - Part B 26(1), 1–13 (1996)Google Scholar
- 10.Lee, K., Jeon, J., Lee, W., Jeong, S., Park, S.: QoS for Web services: Requirements and Possible Approaches. W3C Working Group Note 25 (2003), http://www.w3c.or.kr/kr-office/TR/2003/ws-qos/
- 11.Liu, Y.T., Ngu, A.H.H., Zeng, L.Z.: QoS computation and policing in dynamic Web service selection. In: Proceedings of International Conference on World Wide Web, pp. 165–176. IEEE CS Press, New York (2004)Google Scholar
- 12.Lorpunmanee, S., Sap, M.N., Abdullah, A.H., Chompoo-inwai, C.: An Ant Colony Optimization for Dynamic Job Scheduling in Grid Environment. International Journal of Computer and Information Science and Engineering 1(4), 207–214 (2007)Google Scholar
- 13.Ran, S.: A model for Web services Discovery with QoS. ACM SIGecom Exchanges 4(1), 1–10Google Scholar
- 16.Shen, J., Yuan, S.: Modelling Quality and Spatial Characteristics for Autonomous e-Service Peers. In: The 20th International Conference on Advanced Information Systems Engineering (CAiSE 2008), Forum, Montpellier, France, June 2008, vol. 344, pp. 49–52. CEUR-WS (2008) ISSN: 1613-0073Google Scholar
- 17.Vanrompay, Y., Rigole, P., Berbers, Y.: Genetic algorithm-based optimization of service composition and deployment. In: Proceedings of the 3rd international workshop on Services integration in pervasive environments, pp. 13–17 (2008)Google Scholar
- 18.Web Services Architecture Requirements Working Group (2004), http://www.w3.org/TR/wsa-reqs
- 19.Yuan, S., Shen, J.: Mining E-Services in P2P-based Workflow Enactments. special issue Web Mining Applications in E-commerce and E-services of Online Information Review 32(2), 163–178 (2008)Google Scholar