Survey of Different Ranking and Prediction Techniques for QoS

Increasing popularity of Cloud computing lead to research problem for building high quality cloud application . Quality of service provides valuable information for optimal decision. From the set of functionally equivalent service candidates optimal cloud selection is taken. In this paper different QoS ranking prediction techniques are studied .The techniques are collaborative filtering and cloud rank framework. Collaborative filtering techniques have three methods neighborhood, model based and memory based. In this ranking oriented approach accurate missing value prediction may not lead to accuracy ranking. Real-world invocations are required on the service candidates for obtaining Quality of Service (QoS) values. QoS ranking prediction is framework (Cloudrank framework) that avoids time-consuming and expensive real world service invocation. QoS ranking prediction framework for cloud services by taking advantages of past service usage experiences of other consumers.