An MCDM Approach for Cloud Computing Service Selection Based on Best-Only Method

Mostafa, Ahmed M.

doi:10.1109/access.2021.3129716

Cited by 15 publications

(13 citation statements)

References 59 publications

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“…In this phase, we need to calculate the overall weights of the QoS criteria according to the user requirements using the bestonly method [17]. Initially, we computed the weights for each QoS criterion individually.…”

Section: ) the Best-only Methodsmentioning

confidence: 99%

“…We then aggregate the individual calculated weights to determine the final weights for the QoS criteria (𝐹𝑄𝐶𝑊). The following are the steps we follow to calculate the QoS criteria weights based on BOM [17]:…”

Section: ) the Best-only Methodsmentioning

confidence: 99%

“…Thus, other users with similar service requirements can be guided to choose the most appropriate cloud service. Although most MCDM approaches have been thoroughly validated, they still suffer from some weaknesses, including low comparison consistency, complex comparison systems, and an overall increase in computing complexity, which remains a significant barrier when choosing cloud providers [17]. A number of MCDM approaches suffer from a rank reversal problem [18], [19], which occurs when a CSP is added to or deleted from the cloud service repository, ranking a nonoptimal CSP to an optimal CSP.…”

Section: Introductionmentioning

confidence: 99%

“…The Markov chain is used to record and track the changes in the priorities of the user requirements and to determine their final priorities. Then, the best-only method [17], an MCDM approach, is employed to determine the final weights of the QoS criteria based on pairwise comparisons performed by the cloud user or the decision maker and the final priorities of the user's requirements. Finally, the cloud services are ranked, and the best CSP is selected.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Mostafa

2023

IEEE Access

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Cloud computing technology has undergone tremendous growth in recent years, and there are now many cloud service providers (CSPs). This makes CSP selection a challenging process for cloud users. Further complications arise when users modify the priorities of their requirements. Moreover, concerns such as complex computation, inconsistencies, and rank reversal have been raised in current approaches, resulting in less reliable results. This study presents a new hybrid multiple-criteria decision-making (MCDM) framework for ranking cloud services based on Markov chains combined with the best-only method (BOM). The Markov chain is used to record and track the changes in the priorities of user requirements and determine their final values. Then, the BOM method is utilized to determine the final weights of the QoS criteria based on pairwise comparisons made by the cloud user or decision maker and the final priorities of the user's requirements. Finally, the cloud services are ranked, and the best CSP is selected. The proposed framework was validated using a case study and a real dataset. Performance, consistency, rank conformance, and sensitivity analyses were performed to evaluate the proposed framework. The obtained results prove that the proposed framework is computationally efficient and fully consistent, considers the user requirements and its transition pattern, and is robust to rank reversal.. INDEX TERMS Cloud computing (CC), cloud service selection, QoS criteria, cloud service providers (CSPs), multiple-criteria decision-making (MCDM), best-only method (BOM), Markov chains, Rank reversal.

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Section: ) the Best-only Methodsmentioning

confidence: 99%

Section: ) the Best-only Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Mostafa

2023

IEEE Access

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“…When the AHP is adopted instead, the service provider will be required to provide 153 pairwise comparisons to weight the 18 criteria. When compared to AHP and ANP, BWM produces more consistent results and requires fewer pairwise comparisons, making it better suited to situations involving a large number of criteria [45,46]. Table 2 shows the detailed structures of both AHP and BWM.…”

Section: Bwm For Weighting Criteria and Prioritizing Tasksmentioning

confidence: 99%

Light-Weighted Decision Support Framework for Selecting Cloud Service燩roviders

Aljuhani¹,

Alhubaishy²,

Rahmani³

et al. 2023

Computers, Materials &Amp; Continua

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Multi-criteria decision making (MCDM) is a technique used to achieve better outcomes for some complex business-related problems, whereby the selection of the best alternative can be made in as many cases as possible. This paper proposes a model, the multi-criteria decision support method, that allows both service providers and consumers to maximize their profits while preserving the best matching process for resource allocation and task scheduling. The increasing number of service providers with different service provision capabilities creates an issue for consumers seeking to select the best service provider. Each consumer seeks a service provider based on various preferences, such as price, service quality, and time to complete the tasks. In the literature, the problem is viewed from different perspectives, such as investigating how to enhance task scheduling and the resource allocation process, improve consumers' trust, and deal with network problems. This paper offers a novel model that considers the preferences of both service providers and consumers to find the best available service provider for each consumer. First, the model adopts the best-worst method (BWM) to gather and prioritize tasks based on consumers' and service providers' preferences. Then, the model calculates and matches similarities between the sets of tasks from the consumer's side with the sets of tasks from the provider's side to select the best service provider for each consumer using the two proposed algorithms. The complexity of the two algorithms is found to be O(n 3 ).

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Why Should Not a Decision Analyst be Content with Only ($$n-1$$) Pairwise Comparisons? Echoes from the Literature

Brunelli

2023

Lecture Notes in Operations Research

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An MCDM Approach for Cloud Computing Service Selection Based on Best-Only Method

Cited by 15 publications

References 59 publications

A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Light-Weighted Decision Support Framework for Selecting Cloud Service燩roviders

Why Should Not a Decision Analyst be Content with Only ($$n-1$$) Pairwise Comparisons? Echoes from the Literature

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