A Cloud Service Selection Model Based on User-specified Quality of Service Level

Recently, it emerges lots of cloud services in the cloud service market. After many candidate services are initially chosen by satisfying both the behavior and functional criteria of a target cloud service. Service consumers need a selection model to further evaluate nonfunctional QOS properties of the candidate services. Some prior works have focused on objective and quantitative benchmark-testing of QOS by some tools or trusted third-party brokers, as well as reputation from customers. Service levels have been offered and designated by cloud service providers in their Service Level Agreement (SLA). Conversely, in order to meet user requirement, it is important for users to discover their own optimal parameter portfolio for service level. However, some prior works focus only on specific kinds of cloud services, or require users to involve in some evaluating process. In general, the prior works cannot evaluate the nonfunctional properties and select the optimal service which satisfies both user-specified service level and goals most either. Therefore, the aim of this study is to propose a cloud service selection model, CloudEval, to evaluate the nonfunctional properties and select the optimal service which satisfies both user-specified service level and goals most. CloudEval applies a well-known multi-attribute decision making technique, Grey Relational Analysis, to the selection process. Finally, we conduct some experiments. The experimental results show that CloudEval is effective, especially while the quantity of the candidate cloud services is much larger than human raters can handle.