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The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce the total execution time and (ii) energy consumption to find the best service for the user. The proposed cloud service selection mechanism facilitates reduced energy consumption at cloud servers, thereby reducing the overall heat emission from a cloud data center.

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Resource provisioning in the cloud is the most popular business model for any service provider due to profit that is based on how resources are distributed among users in Industry 4.0. Moreover, much research carried out to provide a better resource provisioning system to service providers; however, efficient resource provisioning may save the environments too. This study presents an extensive analysis of different resource provisioning systems based on concert parameters. More than 250 relative research articles have been considered for this study, out of these 125 articles has been processed for comparative analysis on the basis of different performance metrics. This study highlights the classifications of resource management techniques, objective functions, and open research challenges and issues while analyzing resource management techniques. It also provides the depth knowledge of uses of performance metrics utilization based on its classifications.

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