Special Issue Article Open Access

Achieve Ranking Accuracy Using Cloudrank Framework for Cloud Services

Abstract

Building high Quality cloud applications becomes an immediately required research problem in cloud computing technology. Non-functional performance of cloud services is generally described by Quality-of-Service (QoS). To acquire QoS values, real-world usage of services candidates are generally required. At this time, there is no framework that can allow users to estimate cloud services and rank them based on their QoS values. This paper intends to framework and a mechanism that measures the quality and ranks cloud services for the users. CloudRank framework by taking the advantage of past service usage experiences of other users. So it can avoid the time consuming and expensive real life service invocation. This methodology determines the QoS ranking directly using the two personalized QoS ranking prediction approach namely, CloudRank1 and CloudRank2. These algorithms make sure that the active services are correctly ranked. The core determination is ranking prediction of client side QoS properties, which likely have different values for dissimilar users of the same cloud service. It estimates all the applicant services at the user-side and rank the services based on the observed QoS values.

R.Yuvarani, M.Sivalakshmi

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