A Hybrid Optimization Algorithm for Enhanced Web Services with QoS Measures in Cloud Computing.

Abstract

In the Environment of Big-Data analytics in world-wide, the cloud web-services were deployed in internet and Intranet domains. Moreover cloud computing possess the privileges and acquired rapid development, faces the trust complexities, privacy concepts and security issues which allows to implement the QoS measures in the optimization techniques in the web services selection. The study focusses on selection of component services and employing the efficient algorithm with end to end Quality of measures. The Data diversification and the service characteristics would decline the accuracy level of the measures. In this study a novel Qos measure web-services algorithm implemented the weight attributes and the subjective attributes. This study employs the novel hybrid-optimization algorithm in gaining the privileges of the search randomised-attributes and the implementation of IWO-invasive-weed algorithm. This study also focusses on the Calculation of Quality-of-service measures on the weights of the web-services attributes. Many researches have placed the Implementation of nature inspired concept for the optimization complexities in Big Data and thus employing Eagle-Perching Algorithm in the efficiency enhancement of cloud web-services. The evolution of BES- Bald Eagle-Search were utilized as the nature inspired approach would drive as the efficient technique for optimisation issues which imitates the bald-eagles behaviour. The results have been demonstrated the comparison of the performance metrics with the existing approaches to evaluate the proposed methodology.