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

Cloud computing is an emerging internetbased paradigm of rendering services on payas -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 rankings provides valuable information for making optimal cloud service selection from a set of functionally equivalent 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 prediction of optimal composition of services requested by the user. Taking advantage of the past service usage experiences 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.