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An Integrated Framework for Cloud Service Selection Based on BOM and TOPSIS

Ahmed M. Mostafa*

Computer and Systems Engineering Department, Faculty of Engineering at Helwan, Helwan University, Cairo, 11795, Egypt

* Corresponding Author: Ahmed M. Mostafa. Email:

Computers, Materials & Continua 2022, 72(2), 4125-4142.


Many businesses have experienced difficulties in selecting a cloud service provider (CSP) due to the rapid advancement of cloud computing services and the proliferation of CSPs. Many independent criteria should be considered when evaluating the services provided by different CSPs. It is a case of multi-criteria decision-making (MCDM). This paper presents an integrated MCDM cloud service selection framework for determining the most appropriate service provider based on the best only method (BOM) and technique for order of preference by similarity to ideal solution (TOPSIS). To obtain the weights of criteria and the relative importance of CSPs based on each criterion, BOM performs pairwise comparisons of criteria and also for alternatives on each criterion, and TOPSIS uses these weights to rank cloud alternatives. An evaluation and validation of the proposed framework have been carried out through a use-case model to prove its efficiency and accuracy. Moreover, the developed framework was compared with the analytical hierarchical process (AHP), a popular MCDM approach, based on two perspectives: efficiency and consistency. According to the research results, the proposed framework only requires 25% of the comparisons needed for the AHP approach. Furthermore, the proposed framework has a CR of 0%, whereas AHP has 38%. Thus, the proposed framework performs better than AHP when it comes to computation complexity and consistency, implying that it is more efficient and trustworthy.


Cite This Article

A. M. Mostafa, "An integrated framework for cloud service selection based on bom and topsis," Computers, Materials & Continua, vol. 72, no.2, pp. 4125–4142, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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