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Given the continually increasing amount of commercial Cloud services in the market, evaluation of different services plays a significant role in cost-benefit analysis or decision making for choosing Cloud Computing. In particular, employing suitable metrics is essential in evaluation implementations. However, to the best of our knowledge, there is not any systematic discussion about metrics for evaluating Cloud services. By using the method of Systematic Literature Review (SLR), we have collected the de facto metrics adopted in the existing Cloud services evaluation work. The collected metrics were arranged following different Cloud service features to be evaluated, which essentially constructed an evaluation metrics catalogue, as shown in this paper. This metrics catalogue can be used to facilitate the future practice and research in the area of Cloud services evaluation. Moreover, considering metrics selection is a prerequisite of benchmark selection in evaluation implementations, this work also supplements the existing research in benchmarking the commercial Cloud services.Comment: 10 pages, Proceedings of the 13th ACM/IEEE International Conference on Grid Computing (Grid 2012), pp. 164-173, Beijing, China, September 20-23, 201

The SOA approach is a very popular choice today for the implementation of distributed systems.

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons.

Cloud Computing, as one of the most promising computing paradigms, has become increasingly accepted in industry. Numerous commercial providers have started to supply public Cloud services, and corresponding performance evaluation is then inevitably required for Cloud provider selection or cost-benefit analysis. Unfortunately, inaccurate and confusing evaluation implementations can be often seen in the context of commercial Cloud Computing, which could severely interfere and spoil evaluation-related comprehension and communication. This paper introduces a taxonomy to help profile and standardize the details of performance evaluation of commercial Cloud services. Through a systematic literature review, we constructed the taxonomy along two dimensions by arranging the atomic elements of Cloud-related performance evaluation. As such, this proposed taxonomy can be employed both to analyze existing evaluation practices through decomposition into elements and to design new experiments through composing elements for evaluating performance of commercial Cloud services. Moreover, through smooth expansion, we can continually adapt this taxonomy to the more general area of evaluation of Cloud Computing.