Abstract
Autonomic Computing is an approach to address the complexity and evolution process. It is also defined as high level systems that can manage high-level objectives with minimum human intervention. Autonomic computing approach has become inevitable as we desire highest degree of smartness in every application and every service. IBM has taken initiative for defining aspects of autonomics of self-management which are mainly all self X properties we desire in the application. Various methods to achieve degree of self-management are proposed in different domains. Optimal service selection (OSS) while doing service composition is a crucial task because of the presence of set of similar functionality services. To solve this problem potential candidates can be the techniques of Multi-Attribute Decision Making (MADM) methods. In this paper suitability of some selected MADM methods is analyzed for solving optimal service selection problem. Accuracy and execution time are used as a measure for analyzing the performance.
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References
Wan, J., Yan, H., Suo, H., Li, F.: Advances in cyber physical systems research. KSII Trans. Internet Inf. Syst. 5(11) (2011)
Nikam, S., Ingle, R.: Survey of research challenges in cyber physical systems. Int. J. Comput. Sci. Inf. Secur. 15(11), 192–199 (2017)
Jatoth, C., Gangadharan, G.R., Fiore, U., Buyya, R.: QoS-aware big service composition using MapReduce based evolutionary algorithm with guided mutation. Futur. Gener. Comput. Syst. (2017)
Kalasapur, S., Kumar, M., Shirazi, B.A.: Dynamic service composition in pervasive computing. IEEE Trans. Parallel Distrib. Syst. 18(7), 907–918 (2007)
Tamhane, S.A., Kumar, M., Passarella, A., Conti, M.: Service composition in opportunistic networks. In: 2012 IEEE International Conference on Green Computing and Communications (GreenCom), pp. 285–292. IEEE (2012)
Wu, T., Dou, W., Chunhua, H., Chen, J.: Service mining for trusted service composition in cross-cloud environment. IEEE Syst. J. 11(1), 283–294 (2017)
Nikam, S., Ingle, R.: Comparative study of service composition in CPS and IoT. In: International Conference on Advances in Cloud Computing, pp. 1–7 (2014)
Wang, T., et al.: Automatic and effective service provision with context-aware service composition mechanism in cyber-physical systems. Int. J. Adv. Inf. Sci. Serv. Sci. 4(11), 151–160 (2012)
Wang, T.: A two-phase context-sensitive service composition method with a workflow model in cyber-physical systems. In: Proceedings 17th IEEE International Conference on Computational Science and Engineering, pp. 1475–1482 (2014)
Hellbrück, H., et al.: Name-centric service architecture for cyber-physical systems. In: Proceedings IEEE 6th International Conference on Service-Oriented Computing and Applications, pp. 77–82 (2013)
Huang, J., et al.: Extending service model to build an effective service composition framework for cyber-physical systems. In: IEEE International Conference on Service-Oriented Computing and Applications, pp. 130–137 (2009)
Huan, J., et al.: Towards a smart cyber physical space—context sensitive resource explicit service model. In: 33rd International IEEE Conference on Computer Software and Application, pp. 122–127 (2009)
Wang, S., Zhou, A., Yang, M., Sun, L., Hsu, C.-H.: Service composition in cyber-physical-social systems. IEEE Trans. Emerg. Topics Comput. (2017)
Tzeng, G.H., Huang, J.-J.: MADM Methods and Applications. CRC press
Vyas, G., Chetan, M.: Comparative study of different multicriteria decision-making methods. Int. J. Adv. Comput. Theory Eng. 2, 9–12 (2013)
Amin, K., Johansson, R.: Utilization of multi attribute decision making techniques to integrate automatic and manual ranking options. J. Inf. Sci. Eng. 30, 519–534 (2014)
Umm-e-Habiba, Asghar, S.: A survey on multi-criteria decision making approaches. In: IEEE International Conference on Emerging Technologies (2009)
Garg, S.K., Versteeg, S., Buyua, R.: A framework for ranking of cloud services. Futur. Gener. Comput. Syst. 29, 1012–1023 (2013)
Leung, V.: Automated network selection in a heterogeneous wireless network environment. IEEE Netw. (2007)
Karami, A.: Utilization and comparison of multi attribute decision making techniques to rank Bayesian network options (2011)
Jian, S., et al.: PSI for machine selection in flexible manufacturing cell. In: Proceedings of MATEC Web of Conference, 139 (2017)
Maniya, K., Bhatt, M.G.: A selection of material using a novel type decision-making method: Preference selection index method. Mater. Des. 31(4), 1785–1789 (2010)
Behzadian, M., et al.: A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 39(17), 13051–13069 (2012)
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I am thankful to Dr. D. Y. Patil Institute of Technology for being facilitator to carry out this research.
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Nikam, S., Ingle, R. (2019). Autonomics of Self-management for Service Composition in Cyber Physical Systems. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 851. Springer, Singapore. https://doi.org/10.1007/978-981-13-2414-7_42
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DOI: https://doi.org/10.1007/978-981-13-2414-7_42
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