International Journal of Modern Trends in Engineering and Research (IJMTER)
Due to the vast diversity of available cloud services, it is difficult for a customer to decide which cloud service satisfies their requirements at the most. To ease the customer's cloud service selection process an efficient ranking algorithm is required. Quality of Service (QoS) helps in identifying the customer's requirements and thereby aiding in optimal cloud service selection. The authors propose to analyze three QoS ranking algorithms and identify the issues in these algorithms. In order to overcome the identified constraints, they have designed a hybrid QoS rank identification framework for cloud services; Correlation Coefficient based Probabilistic Latent Semantic Analysis (CCPLSA), by combining two similarity computation methods. CCPLSA takes advantage of the past service usage experiences of other consumers and QoS attributes.