Abstract

Ever increasing complexity, higher demand for pro-activeness, and high speeds of innovation resulting from heavy competition, demand the adoption of more intelligent systems, which are capable of producing optimal results, in all fields. B2C E-Commerce applications are no different. The major challenge in transitioning a brick and mortar business to an online environment is to provide the same user experience as that of a wayside store, like the consultation, up selling, pro-activeness, negotiation, delivery, etc. This requirement has created a lot of intelligent tools and necessitates further evolution of more intelligent tools. Autonomic computing provides the framework for design of independent intelligent self-managing components, and is thereby optimally suited to assist E-Commerce in this journey. This framework can be extensively used to upgrade existing E-Commerce systems with autonomic features. This paper introduces the concept of autonomic computing in e-commerce applications, and provides a generic architecture, with specific focus on self-optimizing characteristics of autonomic computing. Details of concrete implementation of autonomic components in an e-commerce environment are provided. This paper is the specific application scenario of the generic autonomic concept presented in "Autonomic Computing Architecture for Business Applications" [1].

References

• D. Kurian and P. R. Chelliah, "An Autonomic Computing Architecture for Business Applications," in IEEExplore Digital Library, WICT 2012, Trivandrum, 2012.
• J. O. Kephart and D. M. Chess, "The Vision of Autonomic Computing," IEEE Computer Society, pp. 41-50, January 2003.
• Y. Diao, L. J. Hellerstein, S. Parekh and Griffith, "A Control Theory Foundation for Self-Managing Computing Systems," IEEE Journal on Selected Areas in Communication, vol. 23, pp. 2213--2221, 2003.
• R. Storn and K. Price, "Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continous Spaces," Journal of Global Optimization, vol. 11, pp. 341-359, 1997.
• M. Parashar and S. Hariri, "Autonomous Computing: An Overview," pp. 247-259, 2005.
• L. Stojanovic, J. Schneider, A. Maedche and S. Libischer, "The role of ontologies in autonomic computing systems," IBM Systems Journal, vol. 43, pp. 598-616, 2004.
• H. Schmeck, C. Müller-Schloer, E. Cakar, M. Mnif and U. Richter, "Adaptivity and Self-Organisation in Organic Computing Systems," ACM Transactions on Autonomous and Adaptive Systems, vol. 5, no. 10, pp. 1-32, September 2010.
• X. Dong, S. Hariri and L. Xue, "AUTONOMIA: An Autonomic Computing Environment," 2003.
• J. C. Strassner, N. Agoulmine and E. Lehtihet, "FOCALE – A Novel Autonomic Networking Architecture," 2006.
• A. D. Menascé, H. Gomaa, S. Malek and P. J. Sousa, "SASSY: A Framework for Self-Architecting," IEEE Software, pp. 78-85, November 2011.
• D. Ardagna, M. Comuzzi and E. Mussi, "PAWS: A Framework for Executing Adaptive Web-Service Processes," 2007.
• L. Zhen and M. Parashar, "Rudder: An agent-based infrastructure for Autonomic Composition of Grid Applications," 2005.
• M. Rak and A. Cuomo, "CHASE: an Autonomic Service Engine for Cloud Environments," in 20th IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2011.
• February 2012. [Online]. Available: http://www. ipsoft. com/.
• H. S. Venkatarama and C. Kandasamy, "An approach in using differentiated services to Maximise Profit in an autonomic Computing System," International Journal of Computer Applications, vol. 5, no. 8, pp. 22-26, 2010.
• "Google Analytics," google, 15 December 2012. [Online]. Available: http://www. google. co. in/analytics/. [Accessed 15 December 2012].
• "Piwik Analytics," Piwik, [Online]. Available: http://piwik. org/. [Accessed 15 December 2012].
• R. A. C. W. A. S. Thaddeus Eze, "A Technique for Measuring the Level of Autonomicity (LoA) of Self-managing Systems," in ICAS2012, Netherlands, 2012.