Framework for Quality Ranking of Components in Cloud Computing: Regressive Rank

As the popularity of cloud computing is increasing there is an urgent requirement of developing highly efficient and highly qualitative cloud applications (CA). Hence, it be-comes a big research problem. A recommender system recommends the suitable item to the user and almost all the systems provide a rating score for preference. Traditionally, algorithms predicts the ratings that a user should give to the unrated components to queue the item in recommended list. To select an optimal candidate from a set of function-ally equivalent candidates is crucial through approaches that follow a framework for component quality ranking. More-over, such framework helps in detecting the poor performing candidates from a highly distributed cloud applications. In this paper a novel technique is proposed to provide personalized component ranking for designers by employing the past usage of components by different users. In this approach the similarity between the users is measured based on their rankings for functionally equivalent components set instead of their rating values. In this approach no additional invocation of cloud component is required. Experimental results on real world web-service invocations data set shows that the proposed approach outperforms the previous approaches.