Application of Cloud Rank Framework to Achieve the Better Quality of Service (QoS) Ranking Prediction of Web Services

In Cloud computing the QoS rankings provides priceless information for making most favourable cloud service selection from a set of functionally comparable service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and pricey real-world service invocation QoS ranking prediction framework is used. This framework requires no supplementary invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches such as cloud rank 1 ( CR1) and cloud rank 2 ( CR2) are used to envisage the QoS rankings unswervingly. Widespread experiments are conducted employing real-world QoS data, including 1000 distributed users and three real-world web services. The implemented framework uses modernized ranking approach which uses different QoS parameters to predict the ranking more accurately. Normalized Discounted Cumulative Gain (NDCG) has been used to analyze the accuracy of QoS ranking prediction for the implemented framework.