Authors: Chang-Ling Hsu and Chia-Ming Liu
Recently, some cloud service selection focus only on specific kinds of cloud services, or require users to involve in some evaluating process. However, it is important for users to discover the optimal service with their own parameter portfolio for service level. Therefore, we have proposed a cloud service selection model named CloudEval (Cloud Evaluator) before. CloudEval is known as its capability of evaluate the nonfunctional properties and select the optimal service which satisfies both user-specified service level and goals most. As users feel more comfortable to use fuzzy concept to weight among attributes and to rate by linguistic variables of an attribute, a new fuzzy cloud service selection model is required to improve CloudEval. Therefore, the aim of this paper is to develop another cloud service selection model named CloudEval2 (Cloud Evaluator 2). CloudEval applies a well-known multi-attribute decision making technique, Grey Relational Analysis, to the selection process. CloudEval2 has improved CloudEval by combining fuzzy technique with grey relational analyzing technique for the weighting and the rating. The experimental results show that CloudEval2 has improved the correlation coefficients between the rank lists of cloud services selected by all the raters and by CloudEval, especially while the quantity of the candidate cloud services is much larger than human raters can handle.
Keywords: Cloud Service; Cloud Service Selection; Cloud Computing; Multi-attribute Decision Model; Quality of Service