SLA based cloud service composition using genetic algorithm

(1) * N Sasikaladevi Mail (School of Computing, SASTRA University, India)
*corresponding author


Cloud computing tends to provide high quality on-demand services to the users. Numerous services are evolving today. Functionally similar services are having different non-functional properties such as reliability, availability, accessibility, response time and cost. A single service is inadequate for constructing the business process. Such business process is modeled as composite service. Composite service consists of several atomic services connected by workflow patterns. Selecting services for service composition with the constraints specified in Service Level Agreement is the NP-hard problem. Such a cloud service composition problem is modeled in this paper. Genetic based cloud service composition algorithm (GCSC) is proposed. Proposed algorithm is compared with the existing genetic based cloud service composition algorithm based on average utility rate and convergence time. It is proved that the proposed algorithm provides better performance as compared to the existing cloud service composition algorithm.



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Peter Mell, T. G,The NIST Definition of Cloud Computing. NIOS Technology, U.S. Department of Commerce, 2011.

Hamdaqa, M., & Tahvildari, L. Cloud Computing Uncovered: A Research Landscape. Advances in Computers, 86, pp. 41–85,Elsevier, 2012.

Yakimenko, O. A., Slegers, N. J., Bourakov, E. A., Hewgley, C. W., Bordetsky, A. B.,Jensen, R. P., Robinson, A. B., Malone, J. R., & Heidt, P. E., Mobile system for precise aero delivery with global reach network capability. IEEE International Conference on Control and Automation, 2009. ICCA 2009, pp. 1394–1398, 2009.

Wischik, D., Handley, M., & Braun, M. B, The resource pooling principle, SIGCOMM Computer Communication Review, 38, 47–52, 2008

Fei, T., Yuanjun, L., Lida, X., & Lin, Z, FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Transactions on Industrial Informatics, 9, 2023–2033, 2013

Anselmi, J., Ardagna, D., & Cremonesi, P. A QoS-based selection approach of autonomic grid services. In Proceedings of the 2007 workshop on service-oriented computing performance. Aspects, issues, and approaches.pp. 1–8 . Monterey, California, USA: ACM, 2007

Yu, T., & Lin, K.-J. (2005). Service selection algorithms for composing complex services with multiple qos constraints. In Proceedings of the Third International Conference on Service-Oriented Computing, pp. 130–143. Amsterdam, The Netherlands: Springer-Verlag, 2005

Kofler, K., ul Haq, I., & Schikuta, E, A parallel branch and bound algorithm for workflow QoS optimization. International Conference on Parallel Processing, 2009. pp. 478–485.

Preve, N. P, Grid Computing: Towards a Global Interconnected Infrastructure. Springer, 2011

Korte, B., & Vygen, J, Linear Programming . Berlin Heidelberg: Combinatorial Optimization Springer, pp. 51-71, 2012

Vanderbei, R. J,). Linear Programming: Foundations and Extensions. Springer London Limited, 2008.

Liu, M., Wang, M. R., Shen, W. M., Luo, N., & Yan, J. W, A quality of service (QoS)-aware execution plan selection approach for a service composition process. Future Generation Computer Systems-the International Journal of Grid Computing and science, 28, pp.1080–1089, 2013.

Qi, Y., & Bouguettaya, A, Efficient service skyline computation for composite service selection. IEEE Transactions on Knowledge and Data Engineering, 25, pp.776–789, 2013.

Knuth, D. E, The Art of Computer Programming. Volume. 4 A: Combinatorial Algorithms, Part 1, Pearson Education India, 2011.

Shrme, A. E, Hybrid intelligent technique for automatic communication signals recognition using bees algorithm and MLP neural networks based on the efficient features. Expert Systems with Applications, 38, pp.6000–6006, 2011.

Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., & Guo, S.-P, Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38, pp.14346–14355, 2011.

Jungmann, A., & Kleinjohann, B, Towards the Application of Reinforcement Learning Techniques for Quality-Based Service Selection in Automated Service Composition. International Conference on Services Computing (SCC), 2012 IEEE, pp. 701–702, 2012

Ludwig, S. A, Clonal selection based genetic algorithm for workflow service selection. IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 1–7, 2012.

Yang, Y., Mi, Z., & Sun, J, Game theory based IaaS services composition in cloud computing environment. Advances in Information Sciences and Service Sciences, 4, pp.238–246, 2012.

Wang, S. G., Sun, Q. B., Zou, H., & Yang, F. C, Particle swarm optimization with skyline operator for fast cloud-based web service composition. Mobile Networks & Applications, 18, pp.116–121, 2013.

Jula, A., Othman, Z., & Sundararajan, E, A hybrid imperialist competitivegravitational attraction search algorithm to optimize cloud service composition. IEEE Workshop on Memetic Computing (MC), 2013, pp. 37–43, 2013

Bao, H. H., & Dou, W. C, A QoS-aware service selection method for cloud service composition. In 2012 IEEE 26th international parallel and distributed processing symposium workshops & Phd Forum pp. 2254–2261. New York: IEEE, 2012.

Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., & Guo, S.-P, Forecasting stock indices with back propagation neural network. Expert Systems with Applications, 38, pp.14346–14355, 2011.

Ye, Z., Zhou, X., & Bouguettaya, A, Genetic algorithm based QoS-aware service compositions in cloud computing. In J. Yu, M. Kim, & R. Unland (Eds.). Database Systems for Advanced Applications ,6588, pp. 321–334. Berlin Heidelberg: Springer, 2011.

Zhu, Y., Li, W., Luo, J., & Zheng, X, A novel two-phase approach for QoSaware service composition based on history records. IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2012, pp. 1–8.

Worm, D., Zivkovic, M., van den Berg, H., & van der Mei, R, Revenue maximization with quality assurance for composite web services. IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2012, pp. 1–9.

Sundareswaran, S., Squicciarini, A., & Lin, D, A Brokerage-Based Approach for Cloud Service Selection. IEEE 5th International Conference on Cloud Computing (CLOUD), 2012, pp. 558–565.

Chunqing, C., Shixing, Y., Guopeng, Z., Bu Sung, L., & Singhal, S, A Systematic Framework Enabling Automatic Conflict Detection and Explanation in Cloud Service Selection for Enterprises. IEEE 5th International Conference on Cloud Computing (CLOUD), 2012, pp. 883–890.

Xiaona, W., Bixin, L., Rui, S., Cuicui, L., & Shanshan, Q, Trust-Based Service Composition and Optimization. In Asia-Pacific Software Engineering Conference (APSEC), 2012,Vol. 1, pp. 67–72.

Zhang, X. Y., & Dou, W. C, Preference-aware QoS evaluation for cloud web service composition based on artificial neural networks. In F. L. Wang, Z. G. Gong, X. F. Luo, & J. S. Lei (Eds.). Web Information Systems and Mining, 6318, pp. 410–417. Berlin: Springer-Verlag Berlin, 2010.

Karim, R., Chen, D., & Miri, A, An end-to-end QoS mapping approach for cloud service selection. IEEE Ninth World Congress on Services (SERVICES), 2013, pp. 341–348.

Zibin, Z., Yilei, Z., & Lyu, M. R, Distributed QoS Evaluation for Real-World Web Services. IEEE International Conference on Web Services (ICWS), 2010, pp. 83–90.

Xin Zhou, Liwei shen, Xin Peng, Wenyun Zhao, “Towards SLA constrained service composition: An Approach based on a fuzzy linguistic preference model and an evolutionary algorithm”, Information Science, Elsevier, 316, 2015, pp.370-396

Tao Yu, Yue Zhang, Kwei Jay Lin, “Efficient algorithms for web services selection with end-to-end QoS constraints”, ACM transactions on the web, vol. 1 issue 1 2007

Tan, Khor and Lee, Multi objective Evolutionary Algorithms and Applications , Springer-Verlag London Limited 2005

Sivanandam • S.N.Deepa,Introduction to Genetic Algorithms, Springer-Verlag Berlin Heidelberg 2008

Wang, Qibo Sun, Hua Zou, Fangchun Yang, Particle Swarm Optimization with Skyline Operator for Fast Cloud-based Web Service Composition, Mobile Network Applications 18, pp.116–121, 2013.

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International Journal of Advances in Intelligent Informatics
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