O. P. Rishi

Work place: Kota Engineering College, Computer Science Department, Kota 324010, India

E-mail: omprakashrishi@yahoo.com


Research Interests: Computer systems and computational processes, Systems Architecture, Information Systems, Data Mining, Data Structures and Algorithms


O.P. Rishi received a M.Tech. degree from Birla Institute of Technology (BIT), Mesra, Ranchi, India in May 1998 and a Ph.D. degree from the Banasthali University, Jaipur, India. Cur-rently, he is an Associate Professor of Computer Science De-partment at the Kota Engineering College, Kota, India. Dr. Rishi's major research interest lies in service-oriented architec-ture, distributed systems, web and data mining technologies.

Author Articles
A GA-Tabu Based User Centric Approach for Discovering Optimal Qos Composition

By Vivek Gaur Praveen Dhyani O. P. Rishi

DOI: https://doi.org/10.5815/ijmecs.2015.02.08, Pub. Date: 8 Feb. 2015

Cloud computing is an emerging internet-based paradigm of rendering services on pay- as -per -use basis. Increasing growth of cloud service providers and services creates the need to provide a tool for retrieval of the high-quality optimal cloud services composition with relevance to the user priorities. Quality of Service rank-ings provides valuable information for making optimal cloud service selection from a set of functionally equiva-lent service candidates. To obtain weighted user-centric Quality of Service Composition, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes framework for predic-tion of optimal composition of services requested by the user. Taking advantage of the past service usage experi-ences of the consumers more cost effective results are achieved. Our proposed framework enables the end user to determine the optimal service composition based on the input weight for individual service Quality of Service. The Genetic algorithm and basic Tabu search is applied for the user-centric Quality of Service ranking prediction and the optimal service composition. The experimental results proves that our approaches outperform other competing approaches.

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