Cloud computing is growing tremendously for its on-demand services, a massive pool of distributed resources, rapid provisioning of resources, and many more. It empowers many organizations/customers to build on-demand applications without investing large capital in creating hardware infrastructure. These organizations encounter numerous challenges towards obtaining full-pledged services from the cloud service providers (CSPs). One such challenge is identifying and deciding upon a suitable CSP that can fulfill the quality of service (QoS) requirements of these organizations. Moreover, the services offered by the CSPs are interrelated and beneficial, and non-beneficial. As a result, it makes it difficult for organizations to suitably evaluate the services rendered by the CSPs. Therefore, multi-attribute decision-making (MADM) algorithms are applied in the literature to overcome the above challenge of uncertainty. In this paper, we survey applications of such algorithms from the perspective of cloud computing. The survey covers both traditional and recent algorithms with their objectives, processes, pros, cons, and implementations. We also present the upcoming challenges and open issues, followed by the performance metrics and tools for their possible implementations. Finally, we conclude by summarizing the survey with some notable remarks.
Recent advancements in information technology (IT) have made cloud computing one of the most prominent technologies. It is most favor- able for the bundle of services that it provides to its users. Since there is a wide range of cloud service providers (CSPs) with various services, it is chal- lenging for the user to select a CSP that can meet all of its requirements. In this paper, we propose a composite cloud service (CCS) model, which is handled by a cloud agent, to identify the best cloud services/criteria from a set of CSPs by considering the objective and subjective opinions collected from the cloud users’ feedback and reviews. Note that the cloud agent is an intermediary between the users and CSPs. Then the agent recommends the CSPs to assemble the identified services into unified group services to fulfill the users’ requirements. Our model calculates the integrated objective and subjective weight of alternatives for a set of criteria and determines the best alternative for each criterion. For this, the application of two multi-criteria decision-making (MCDM) techniques, namely method based on the removal effect of criteria (MEREC) and extended step-wise weight assessment ra- tio analysis (ESWARA), are used to calculate the objective and subjective weights, respectively. The proposed model is compared with the analytic hier- archy process-technique for order of preference by similarity to ideal solution (AHP-TOPSIS), TOPSIS-VlseKriterijuska Optimizacija I Komoromisno Re- senje (VIKOR), and SWARA-VIKOR to show its effectiveness.
Recent advancements in information technology (IT) have made cloud computing one of the most prominent technologies. It is most favorable for the bundle of services that it provides to its users. Since there is a wide range of cloud service providers (CSPs) with various services, it is challenging for the user to select a CSP that can meet all of its requirements. In this paper, we propose a composite cloud service (CCS) model, which is handled by a cloud agent, to identify the best cloud services/criteria from a set of CSPs by considering the objective and subjective opinions collected from the cloud users' feedback and reviews. Note that the cloud agent is an intermediary between the users and CSPs. Then the agent recommends the CSPs to assemble the identified services into unified group services to fulfill the users' requirements. Our model calculates the integrated objective and subjective weight of alternatives for a set of criteria and determines the best alternative for each criterion. For this, the application of two multi-criteria decision-making (MCDM) techniques, namely method based on the removal effect of criteria (MEREC) and extended step-wise weight assessment ratio analysis (ESWARA), are used to calculate the objective and subjective
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