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Abstract
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.
Highlights
There are three main cloud service models: infrastructure as a service (IaaS), software as a service (SaaS), and platform as a service (PaaS) [4] and the cloud services taken from the provider in the form of “as a service”
The outcome of cloud service selection and recommendation system (CS-SR) is two-fold. (a) Offering a quality of service (QoS)-based service selection, (b) reducing overall execution time required to find optimal service; The filtration phase will reduce unnecessary comparison by filtering out candidate services; The proposed approach makes use of quantitative and qualitative attributes that will improve the overall efficiency of our selection and recommendation approach; The proposed CS-SR is compared with the analytical hierarchy process (A.H.P.) [38], efficient non-dominated sorting-sequential search (ENS-SS) [39], and best-worst method (B.W.M.) [40] on the performance parameter: total execution time and the energy consumption used in selecting and recommending the cloud service
Fitness Function range, and the user priority is related to the QoS attributes
Summary
Existing work proposed by different researchers using an evolutionary algorithm for selecting cloud services is shown in the work of [23,24,25,26]. (a) Offering a QoS-based service selection, (b) reducing overall execution time required to find optimal service; The filtration phase will reduce unnecessary comparison by filtering out candidate services; The proposed approach makes use of quantitative and qualitative attributes that will improve the overall efficiency of our selection and recommendation approach; The proposed CS-SR is compared with the analytical hierarchy process (A.H.P.) [38], efficient non-dominated sorting-sequential search (ENS-SS) [39], and best-worst method (B.W.M.) [40] on the performance parameter: total execution time and the energy consumption used in selecting and recommending the cloud service. The research is based on providing energy-efficient cloud service selection recommendation system.
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