## 2 Related Work  
In a cloud environment, different stakeholders have different interests, which presents a major challenge [12]. Adopting techniques such as the MCDM method can help decision makers overcome these challenges. Additionally, the effectiveness and efficiency of job offloading can be enhanced by adopting MCDM methods, such as AHP [13; 14]. [PERSON] et al. [15] stated that the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) and the AHP are the most commonly adopted MCDM techniques in cloud environments. The problem of cloud task scheduling was investigated by [PERSON] et al. [16] based on the integration of an MCDM method--namely, the Best Worst Method (BWM)--and the Vlekrierijumsko KOmpromismo Rangiranje (VIKOR) (or Multi-criteria Optimization and Compromise Solution) method. Task priorities were specified by implementing the VIKOR method and by considering various levels of priorities, such as the resource, scheduling, and task levels. The authors argued that the integration of the BWM and VIKOR enhanced factors such as the waiting time, throughput, and the utilization of VMs. [PERSON] et al. [17] integrated the double-normalization-based multiple aggregation (DNMA) approach into the Z-number environment to overcome limitations in the existing MCDM method for CSP selection. The presented method considered various qualitative and quantitative factors, such as cost and target types, for sustainable service provider development. [PERSON] et al. [18] ranked different CSPs based on a combined compromise solution (CoCoSo) method. The authors introduced a maximum variance optimization framework to ensure consistent results. [PERSON] et al. [19] compared different MCDM techniques, such as TOPSIS, the AHP, VIKOR, and the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) II, to find the most efficient method in terms of time consumption and robustness in a cloud environment. The results showed that the AHP and TOPSIS were equivalent in terms of robustness. [PERSON] et al. [20] proposed a workflow scheduling algorithm for a cloud environment based on the integration of TOPSIS with the entropy weight method (EWM). The proposed method considered various significant criteria, such as reliability, time, cost, and energy consumption. The main objective of their study was to reduce costs, energy consumption, and execution time. Based on the VIKOR approach, [PERSON] et al. [21] provided a service broker policy for choosing the optimal data center in cloud computing. Through consideration of variables including cost, response time, and data center processing time, the policy sought to maximize user priorities. A multi-criteria-based resource score heuristic for cloud workflow scheduling was introduced by [PERSON] [22]. The heuristic was created with the aim of lowering the makespan while taking the probability of the physical resources' temporal availability for the VMs into consideration. Following the creation of an initial schedule based on a priority list of tasks, the task-to-VM mapping was refined in real time to adapt to dynamic changes in the availability of cloud resources. A comparative investigation of MCDM techniques in the backfilling process for executing tasks with deadlines in a cloud environment was proposed by [PERSON] et al. [23]. In the backfilling algorithm, proper resources were supplied by the backfill tasks. When there were similar tasks, some of the tasks were classified as backfill tasks, resulting in a difficult problem. The backfilling algorithm was simulated alongside the AHP, VIKOR, and TOPSIS to prevent scheduling conflicts among identical tasks. [PERSON] and [PERSON] [24] presented a mapping/scaling technique based on the AHP to solve strategic decision problems in a cloud environment. The proposed model considered six criteria, including risk, financial benefits, core competency, and customer satisfaction. These criteria were weighted to evaluate three alternatives, which were integrated, hardware, and no entry.