Abstract
Cloud computing has grown as a computing paradigm in the last few years. Due to the explosive increase in the number of cloud services, QoS (quality of service) becomes an important factor in service filtering. Moreover, it becomes a non-trivial problem when comparing the functionality of cloud services with different performance metrics. Therefore, optimal cloud service selection is quite challenging and extremely important for users. In the existing approaches of cloud service selection, the user’s preferences are offered by the user in a quantitative form. With fuzziness and subjectivity, it is a hurdle task for users to express clear preferences. To address this challenge, in this paper, we proposed a hybrid Multi-Criteria Decision Making (MCDM) methodology to aid the decision maker to evaluate different cloud services based on subjective and objective assessments. To do that, we introduced a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method that combines subjective and objective aspects. We used the entropy weight method to do an objective assessment in order to reduce the influence of erroneous or fraudulent Quality of Service (QoS) information. For subjective assessment, we employed a systematic MCDM method called Best Worst Method (BWM). In the end, a numerical example is shown to validate the effectiveness and feasibility of the proposed methodology.
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Mohapatra, S.S., Kumar, R.R. (2022). A Framework for Ranking Cloud Services Based on an Integrated BWM-Entropy-TOPSIS Method. In: Mohanty, M.N., Das, S., Ray, M., Patra, B. (eds) Meta Heuristic Techniques in Software Engineering and Its Applications. METASOFT 2022. Artificial Intelligence-Enhanced Software and Systems Engineering, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-11713-8_29
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