Achieve Better Ranking Accuracy Using CloudRank Framework for Cloud Services
This addresses the need for efficient cloud service selection for users, but it appears incremental as it builds on prior work like CloudRank2.
The paper tackles the problem of personalized QoS ranking for cloud services without expensive real-world invocations by proposing the CloudRank framework, which predicts rankings directly and achieves better accuracy than the CloudRank2 algorithm.
Building high quality cloud applications becomes an urgently required research problem. Nonfunctional performance of cloud services is usually described by quality-of-service (QoS). In cloud applications, cloud services are invoked remotely by internet connections. The QoS Ranking of cloud services for a user cannot be transferred directly to another user, since the locations of the cloud applications are quite different. Personalized QoS Ranking is required to evaluate all candidate services at the user - side but it is impractical in reality. To get QoS values, the service candidates are usually required and it is very expensive. To avoid time consuming and expensive realworld service invocations, this paper proposes a CloudRank framework which predicts the QoS ranking directly without predicting the corresponding QoS values. This framework provides an accurate ranking but the QoS values are same in both algorithms so, an optimal VM allocation policy is used to improve the QoS performance of cloud services and it also provides better ranking accuracy than CloudRank2 algorithm.