Abstract
Currently, with the rapid development and broad application of cloud computing technology, companies tend to use cloud services to build their applications or business systems. Selecting a trustworthy cloud service is a challenging multi-criteria decision-making (MCDM) problem. Moreover, decision makers are more inclined to use linguistic descriptions to assess the quality of service (QoS) for cloud services due to the limitation of the decision makers' knowledge and the vagueness of criteria information. Therefore, we propose a practical, integrated MCDM scheme for cloud service evaluation and selection of cloud systems, allowing decision makers to compare cloud services based on QoS criteria. First, to more accurately and effectively express the uncertainty of qualitative concepts, the cloud model is used as a conversion tool for qualitative and quantitative information to quantify linguistic terms. Second, given the shortcomings of traditional differentiating measures between cloud models, a more comprehensive distance measurement algorithm using cloud droplet distribution is proposed for the cloud model. The new distance measurement algorithm is applied to the calculation of cloud model similarity and the gray correlation coefficient. The dynamic expertise weights are determined by calculating the similarity between the expert evaluation cloud model and the arithmetic mean cloud model. Then, we propose a technique for order preference by similarity to an ideal solution (TOPSIS) improved by the grey relational analysis (GRA) to calculate the relative closeness of alternatives to the positive and negative ideal solutions and establish a multi-objective optimization model that maximizes the relative closeness of all alternatives to determine the weights of the criteria. Finally, we reconstructed the QoS evaluation criteria for cloud services from both application and service perspectives, and the classical TOPSIS is applied to generate alternative rankings. The practicability and robustness of the scheme were tested through the cloud service selection problem experienced by a real mining company's scheduling platform, which can provide practical references with the theoretical basis for the selection and evaluation of cloud services.
Highlights
With the development and widening application of Internet technology as well as the demands created by modern big data collection, the demand for more powerful Internet data processing capabilities is increasing, and “cloud computing” technology has gradually become the focus of the computer technologies field [1], [2]
The dynamic expert weights are determined by calculating the similarity between the expert evaluation cloud model and the arithmetic mean cloud model
The cloud service ranking results of our proposed multi-criteria decision-making (MCDM) scheme are completely consistent with the ranking results obtained through the improved VIKOR method and the improved grey relational analysis (GRA) method
Summary
With the development and widening application of Internet technology as well as the demands created by modern big data collection, the demand for more powerful Internet data processing capabilities is increasing, and “cloud computing” technology has gradually become the focus of the computer technologies field [1], [2]. Cloud computing integrates many computing resources, storage resources, and software resources through information technologies such as distributed computing, utility computing, parallel computing, VOLUME XX, 2019. Li Liu et al.: A Practical, Integrated Multi-Criteria Decision-Making Scheme for Choosing Cloud Services in Cloud Systems grid computing, and virtualization, forming a colossal virtual shared resource pool to provide users with the information they need [3]-[5]. Cloud services can achieve rapid supply and release of resources such as computing networks, servers, hardware, and software through minimal management or interaction with service providers [7]. Relying on the advanced service concept of cloud computing, enterprises or individual users tend to use cloud services to build their business systems or personal applications and establish new development models
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