Abstract In the Web Service Composition (WSC) domain, Web Services (WSs) execute in a highly dynamic environment, as a result, the Quality of Service (QoS) of a WS is constantly evolving, and this requires tracking of the global optimization overtime to satisfy the users’ requirements. In order to make a WSC adapt to such QoS changes of WSs, we propose a self- healing model for WSC. Self-healing is the automatic discovery, and healing of the failure of a composite WS by itself due to QoS changes without interruption in the WSC and any human intervention. To the best of our knowledge, almost all the existing self-healing models in this domain substitute the faulty WS with an equivalent one without paying attention to the WS selection processes to achieve global optimization. They focus only on the WS substitution strategy. In this paper, we propose a self-healing model where we use our hybrid approach to find the optimal WSC by using Parallel Genetic Algorithm based on Q-learning, which we integrate with K-means clustering (PGAQK). The components of this model are organized according to IBM’s Monitor, Analyse, Plan, Execute, and Knowledge (MAPE-K) reference model. The PGAQK approach considers as a module in the Execute component. WS substitution strategy has also been applied in this model that substitutes the faulty WS with another equivalent one from a list of candidate WSs by using the K-means clustering technique. K-means clustering is used to prune the WSs in the search space to find the best WSs for the environment changes. We implemented this model over the NET Framework using C# programming language. A series of comparable experiments showed that the proposed model outperforms improved GA to achieve global optimization. Our proposed model also can dynamically substitute the faulty WSs with other equivalent ones in a time-efficient manner.

References

[1] Abbassi I., Graiet M., Boubaker S., Mourad K., and Hadj-Alouane N., “A Formal Approach for Verifying QoS Variability in Web Services Composition using EVENT-B,” in Proceedings of the IEEE International Conference on Web Services, New York, pp. 519-526, 2015.

[2] Al-Masri E. and Mahmoud Q., “Discovering the Best Web Service,” in Proceedings of the 16th International Conference on World Wide Web, New York, pp. 1257-1258, 2007.

[3] Al-Masri E. and Mahmoud Q., “QoS-based Discovery, and Ranking of Web Services,” in Proceedings of 16th International Conference on Computer Communications and Networks, Honolulu, pp. 529-534, 2007.

[4] Angarita R., Cardinale Y., and Rukoz M., “Reliable Composite Web Services Execution: Towards a Dynamic Recovery Decision,” Electronic Notes in Theoretical Computer Science, vol. 302, pp. 5-28, 2014.

[5] Boumhamdi K. and Jarir Z., “An Approach to Support Monitoring, and Recovery of BPEL Processes at Runtime,” International Journal of Computer Applications, vol. 43, no. 2, pp. 34-41, 2012.

[6] Cardellini V., Casalicchio E., Grassi V., Iannucc S., Presti F., and Mirandola R., “MOSES: a Platform for Experimenting with QoS-driven Self-adaptation Policies for Service Oriented Systems,” in Proceedings of Software Engineering for Self-Adaptive Systems 3. Assurances, Germany, pp. 409-433, 2013.

[7] Dai Y., Yang L., and Zhang B., “QoS-Driven Self-Healing Web Service Composition Based on Performance Prediction,” Journal of Computer Science, and Technology, vol. 24, no. 2, pp. 250- 261, 2009.

[8] Elsayed D., Nasr E., Ghazali A., and Gheith M., “A New Hybrid Approach using Genetic Algorithm, and Q-learning for QoS-aware Web Service Composition,” in Proceedings of the 3rd International Conference on Advanced Intelligent Systems, and Informatics, Cairo, pp. 537-546 2017.

[9] Elsayed D., Nasr E., Ghazali A., and Gheith M., “Integration of Parallel Genetic Algorithm, and Q-learning for QoS-aware Web Service Composition,” in Proceedings of the 12th International Conference on Computer Engineering, and Systems, Cairo, pp. 221-226, 2017.

[10] Elsayed D., Nasr E., Ghazali A., and Gheith M., “PGAQK: An Adaptive QoS-aware Web Service Composition Approach,” International Journal of Intelligent Engineering, and Systems, vol. 11, no. 4, pp. 231-240, 2018.

[11] Gupta S. and Bhanodia P., “A Fault Tolerant Mechanism for Composition of Web Services using Subset Replacement,” International Journal of Advanced Research in Computer, and Communication Engineering, vol. 2, no. 8, pp. 3080-3085, 2013.

[12] Jatoth C., Gangadharan G., and Buyya R., “Computational Intelligence based QoS-aware Web Service Composition: A Systematic Literature Review,” IEEE Transactions on Services Computing, vol. 10, no. 3, pp. 475-492, 2015.

[13] Jayashree K. and Anand S., “Run Time Fault Detection System for Web Service Composition, 846 The International Arab Journal of Information Technology, Vol. 17, No. 6, November 2020 and Execution,” Smart Computing Review, vol. 5, no. 5, pp. 469-482, 2015.

[14] Karray M., Ghedira C., and Maamar Z., “Towards a Self-Healing Approach to Sustain Web Services Reliability,” in Proceedings of the Workshops of International Conference on Advanced Information Networking, and Applications, Singapore, pp. 267-272, 2011.

[15] Li G., Liao L., Song D., Wang J., Sun F., and Liang G., “A Self-healing Framework for QoS- Aware Web Service Composition via Case-Based Reasoning,” in Proceedings of Asia-Pacific Web Conference, Sydney, pp. 654-661, 2013.

[16] Liu H., Zhong F., Ouyang B., and Wu J., “An Approach for QoS-aware Web Service Composition based on Improved Genetic Algorithm,” in Proceedings of the International Conference on Web Information Systems, and Mining, Sanya, pp. 123-128, 2010.

[17] Poonguzhali S., JerlinRubini L., and Divya S., “A Self-Healing Approach for Service Unavailability in Dynamic Web Service Composition,” The International Journal of Computer Science, and Information Technologies, vol. 5, no. 3, pp. 4381- 4383, 2014.

[18] Rajendran V., Chua F., and Chan G., “Self- Healing in Dynamic Web Service Composition,” in Proceedings of the 5th International Conference on Future Internet of Things, and Cloud, Prague, pp. 206-211, 2017.

[19] Rastegari Y. and Shams F., “A Dynamic Architecture for Runtime Adaptation of Service- based Applications,” The International Arab Journal of Information Technology, vol. 16, no. 3, pp. 397-406, 2019.

[20] Subramanian S., Thiran P., Narendra N., Mostefaoui G., and Maamar Z., “On the Enhancement of BPEL Engines for Self-Healing Composite Web Services,” in Proceedings of the International Symposium on Applications, and the Internet, Turku, pp. 33-39, 2008.

[21] Wang H., Chen X., Wu Q., Yu Q., Zheng Z., and Bouguettaya A., “Integrating On-policy Reinforcement Learning with Multi-agent Techniques for Adaptive Service Composition,” in Proceedings of the 12th International Conference Service Oriented Computing, Paris, pp. 154-168, 2014.

[22] Wang H., Wang X., Hu X., Zhang X., and Gu M., “A Multi-Agent Reinforcement Learning Approach to Dynamic Service Composition,” Journal of Information Sciences, vol. 363, pp. 96- 119, 2016.

[23] Wang H., Wu Q., Chen X., Yu Q., Zheng Z., and Bougu A., “Adaptive, and Dynamic Service Composition via Multi-agent Reinforcement Learning,” in Proceedings of the IEEE International Conference on Web Services, Anchorage, pp. 447-454, 2014.

[24] Wang H., Zhou X., Zhou X., Liu W., and Li W., “Adaptive, and Dynamic Service Composition Using Q-Learning,” in Proceedings of the 22nd International Conference on Tools with Artificial Intelligence, Arras, pp. 145-152, 2010.

[25] Xia Y., Chen P., Bao L., Wang M., and Yang J., “A QoS-Aware Web Service Selection Algorithm Based On Clustering,” in Proceedings of the IEEE International Conference on Web Services, Washington, pp. 428-435, 2011.

[26] Zou G., Lu Q., Chen Y., Huang R., Xu Y., and Xiang Y., “QoS-Aware Dynamic Composition of Web Services using Numerical Temporal Planning,” IEEE Transactions on Services Computing, vol. 7, no. 1, pp. 18-31, 2014. Doaa Elsayed received her Ph.D. degree in Information Systems, and Technology from Cairo University, Egypt, in 2019. She is currently a Lecturer at the Computer, and Information Systems Department in Sadat Academy for Management Sciences. Her research interests include web services, requirements engineering, optimization algorithms, and data mining. Eman Nasr is a Ph.D. degree holder in Computer Science. She is currently an Independent Scholar. Her research interests include software engineering, requirements engineering, and embedded software systems. Alaa El Ghazali is currently a Professor of Computer and Information Systems at Sadat Academy for Management Sciences, Egypt. His research interests include software engineering economics, decision support systems, business intelligence, and mobile computing. Mervat Gheith is currently an Associate Professor in the Department of Computer Science in the Faculty of Graduate Studies for Statistical Research at Cairo University, Egypt.