Search

Resource matching and recommendation is an important topic in the field of cloud computing. While a lot of cloud resource discovery and negotiation models have been proposed, resource matching and recommendation issues have often been neglected, such as the utilization of attribute weights and the collaborative application of empirical data, price utility and so on. To cope with this challenge, we focus on designing a novel resource recommendation method which can regulate multi-attribute matching between provider solutions and customer demands in this paper. At first, we describe a resource matching algorithm that considers both functional requirements and QoS attributes. Then, we propose a resource recommendation method for cloud computing system that integrates price utility, multi-attribute matching metric and group customer evaluation. Finally, the extensive simulation results demonstrate that our proposed method is effective in various simulated scenarios. Current results are of high significance to design an efficient resource matching and recommendation with guaranteed QoS requirements under the realistic cloud computing circumstances. [ABSTRACT FROM AUTHOR]

Abstract: The collection and combination of assessment data in trustworthiness evaluation of cloud service is challenging, notably because QoS value may be missing in offline evaluation situation due to the time-consuming and costly cloud service invocation. Considering the fact that many trustworthiness evaluation problems require not only objective measurement but also subjective perception, this paper designs a novel framework named CSTrust for conducting cloud service trustworthiness evaluation by combining QoS prediction and customer satisfaction estimation. The proposed framework considers how to improve the accuracy of QoS value prediction on quantitative trustworthy attributes, as well as how to estimate the customer satisfaction of target cloud service by taking advantages of the perception ratings on qualitative attributes. The proposed methods are validated through simulations, demonstrating that CSTrust can effectively predict assessment data and release evaluation results of trustworthiness. [Copyright &y& Elsevier]