## 2 Related Work
Achieving a QoS that met customers' requirements within a certain cost range by using the GA was addressed by [PERSON] and [PERSON] [4]. SELCLOUD is a framework proposed by [PERSON] et al. [25] to help with selecting the best CSP based on the integration of the AHP with Grey TOPSIS. An uncertainty-based approach for edge computing with balanced resource provision and cost regarding energy utilization was developed by [PERSON] et al. [26]. In order to accomplish cost- and energy-effective dynamic resource allocation, multi-objective dynamic allocation with balanced scheduling methods were adopted. [PERSON] [27] presented a model for selecting the best CSP based on the integration of the BWM with TOPSIS. Various significant criteria, such as response time, security, maintainability, reliability, sustainability, usability, cost, and scalability, were evaluated by using BWM-TOPSIS. [PERSON] et al. [28] introduced a model for selecting a suitable CSP based on the AHP integrated with TOPSIS. The authors used an AHP importance scale ranging from one to nine to determine the weight of each service provider while considering a number of attributes, such as data privacy, accuracy, assurance of data integrity, and reliability. [PERSON] et al. [29] evaluated the interdependence of criteria with regard to cloud service selection with criterion interactions. The interdependencies of the criteria were evaluated based on a fuzzy measure and the Choquet integral, and the importance of the criteria was determined by using pairwise comparisons. A Fuzzy-Euclidean-Taxicab-distance-based method was proposed by [PERSON] [30] to select the suitable cloud deployment model. The introduced model consisted of four cloud deployment models as alternatives, as well as three decision parameters that included 17 sub-parameters. [PERSON] et al. [31] proposed a cloud broker architecture for selecting the best service provider while considering changes in cloud consumers' preferences. The authors adopted the BWM to rank the available services, and the findings showed that better performance could be achieved when using the BWM rather than other MCDM techniques, such as the AHP. [PERSON] and [PERSON] [32] proposed a framework for prioritizing cloud tasks while considering consumers' preferences by using the BWM. The proposed framework allowed consumers to evaluate a broad range of criteria with more flexibility in terms of adding or altering criteria to meet their preferences. [PERSON] and [PERSON] [33] studied the management of energy consumption in a cloud environment when executing applications. The authors integrated the fuzzy AHP with TOPSIS to prioritize various cloud solutions with respect to consumers' requirements. [PERSON] et al. [34] introduced the integer multiplication (IM) method based on the AHP as a simplified and effective prioritization technique. IM assisted cloud consumers with making quick decisions with respect to their preferences when selecting a suitable CSP. In addition, the IM method was used to evaluate important decision-making criteria, such as performance, safety, memory, and cost, as well as several sub-criteria, such as availability, reliability, throughput, power consumption, response time, and bandwidth. The authors compared the IM method's results with those of the existing geometric mean (GM) method to prove the power of the IM method over the GM method. [PERSON] et al. [35] used the ANP integrated with VIKOR to determine the best CSP. The ANP is a general version of the AHP, and it allows decision makers to model decision elements in different clusters. The authors used the ANP to rank local service providers and used VIKOR for the global ranking. The ANP network contained criteria such as accountability, agility, flexibility, adaptability, security, performance, and serviceability. These criteria were evaluated with respect to different goals, such as benefits and opportunities, to rank three service providers and select the most suitable one.