Efficient Algorithm for Predicting QOS in Cloud Services

Cloud computing model enables accessing to information resources in request time. On the other hand, there are different cloud Service providers which present services with different qualitative characteristics. Determining the best cloud computing service for a specific application is a serious problem for users. Ranking compares the different services offered by different providers based on quality of services, in order to select the most appropriate service. In this paper, the existing approaches for ranking cloud computing services are analyzed. The overall performance of each method is presented by reviewing and comparing of them. The essential features of an efficient rating mechanism are indicated. Cloud computing is becoming valuable now a days. Make high-performance based cloud applications is a critical research problem. QoS rankings provide essential information for making optimal cloud service selection from a set of functionally equivalent service candidates. For getting QoS values, real-world invocations on the service candidates are usually essential. To avoid the time-consuming factors and expensive real-world services invocations, this paper develop a QoS ranking prediction architecture for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed architecture requires no additional invocations of cloud services when making QoS ranking prediction. Here we develop two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted for performance analysis. The experimental results show that our approach performance is efficient than other competing approaches.