Cloud computing is a service model that allows hosting and on demand distribution of computing resources all around the world, via Internet. Thus, cloud computing has become a successful paradigm that has been adopted and incorporated into virtually all major known IT companies (e.g., Google, Amazon, Microsoft). Based on this success, a large number of new companies were competitively created as providers of cloud computing services. This fact hindered the clients' ability to choose among those several cloud computing providers the most appropriate one to attend their requirements and computing needs. This work aims to specify a logical/mathematical multi-criteria scoring method able to select the most appropriate(s) cloud computing provider(s) to the user (customer), based on the analysis of performance indicator values desired by the customer and associated with every cloud computing provider that supports the demanded requirements. The method is a three stages algorithm that evaluates, scores, sorts and selects different cloud providers based on the utility of their performance indicators for each specific user of the method. An example of the method's usage is given in order to illustrate its operation.
Cloud Computing popularization inspired the emergence of many new cloud service providers. The significant number of cloud providers available drives users to complex or even impractical choice of the most suitable one to satisfy his needs without automation. The Cloud Provider Selection (CPS) problem addresses that choice. Hence, this work presents a general approach for solving the CPS problem using as selection criteria performance indicators compliant with the Cloud Service Measurement Initiative Consortium - Service Measurement Index framework (CSMIC-SMI). To accomplish that, deterministic (CPS-Matching and CPS-DEA), stochastic (Evolutionary Algorithms: CPS-GA, CPS-BDE, and CPS-DDE), and hybrid (Matching-GA, Matching-BDE, and Matching-DDE) selection optimization methods are developed and employed. The evaluation uses a synthetic database created from several real cloud provider indicator values in experiments comprising scenarios with different user needs and several cloud providers indicating that the proposed approach is appropriate for solving the cloud provider selection problem, showing promising results for a large-scale application. Particularly, comparing which approach chooses the most appropriate cloud provider the better, the hybrid one presents better results, achieving the best average hit percentage, dealing with simple and multi-cloud user requests.
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