## 2 Literature Review
The authors of [13] proposed a hybrid technique to tackle the problem of workflow scheduling. The problem was considered as a multi-objective optimization considering the cost, makespan, and load balancing as measurement parameters. PSO was used for optimization. In the first phase, pre-processing was used, followed by optimization. The proposed algorithm was validated with comparative experimental results. The authors of [14] presented a novel solution for VM allocation in cloud data centers. The method was based on the Krill Herd algorithm. The algorithm uses VM aggregation and host shutdown for power management. During this process, QoS is maintained. Efficient integration and the selection of convenient VM migration strategies reduce energy consumption. The results showed that the proposed method reduced energy consumption compared to other algorithms. A scheduling solution based on a non-linear mixed-integer programming model was presented in [15]. The proposed mechanism makes a tradeoff between execution time and energy consumption during the resource allocation phase. A backward technique is applied to adjust task scheduling and achieve the desired goals. The experimental results showed that the proposed approach significantly reduced the computation time for large sizes of parallel applications. [PERSON] et al. [16] proposed a Multi-Objective Genetic Algorithm (MOGA) that was used to schedule workflows in cloud environments. The MOGA gave a solution that reduced the makespan and provided an energy-efficient solution in a cloud environment. The results of the proposed algorithm showed a significant enhancement in terms of budget, deadline, and energy consumption. The technique also improved resource utilization. [PERSON] et al. [17] proposed a Hybrid PSO (HPSO) method for minimizing the execution time and execution cost in cloud computing. The algorithm is a hybridization of the Budget and Deadline Constraint Heterogeneous Earliest Finish Time (BDHEFT) and multi-objective PSO. The HPSO optimizes the two contradictory parameters--makespan and cost--under the budget and deadline constraint. In addition to these two parameters, energy consumption is also considered as a parameter. The proposed algorithm gives a set of the best solutions, from which the user can select the best solution. In another study [18], a parallel Genetic Algorithm (GA) was used to solve the multi-objective optimization problem of workflow scheduling. The makespan, cost, and load balancing were the targeted parameters. The proposed algorithm first calculates the best solution for each parameter in parallel, and then the best solution is found for all parameters. The proposed method was evaluated with comparative experimental results. Another algorithm that minimizes energy consumption and Service-Level Agreement Violations (SLAVs) during VM consolidation was proposed in [19]. The authors used an energy-aware VM consolidation algorithm that minimized SLAVs. The algorithm uses a fine-tuned prediction model for VM migration, CPU and memory utilization prediction, and target host selection. The proposed method was evaluated with the Planet Lab datasets. The comparative results showed that the proposed algorithm significantly reduced energy consumption and SLAVs. The authors of [20] presented a technique for VM placement that was based on an improved permutation-based GA and a multidimensional resource-aware best-fit allocation strategy. The objective of the algorithm was to reduce energy consumption by reducing the active hosts. The comparative results showed that the proposed mechanism consumed less energy with fewer active hosts in comparison to other methods. Moreover, the proposed method also yielded better resource utilization and load balancing. Another technique based on prediction and dynamic resource-table-updating mechanisms was proposed in [21]. The objective of the proposed method was energy efficiency in task scheduling. The algorithm considers the tasks' arrival times and the sizes of the tasks for efficient scheduling.