## 2 Literature Review
The results are computed in terms of the completion time and response time. The simulation results showed the efficiency of the proposed algorithm over the other techniques with which it was compared. The authors of [22] proposed a task scheduling algorithm and workload classification architecture for VM placement in cloud data centers. The objectives of the technique were resource utilization and energy consumption. The proposed algorithm was validated with comparative experimental results. Cloud data centers demand a huge amount of energy, which changes at different hours of operation, together with the utilization of resources. A mechanism for handling the energy consumption in different operating hours was proposed in [23]. The proposed technique uses frequency scaling and non-power-aware hosts to achieve the desired objectives. VM consolidation policies, i.e., the utilization of local regression minimization and static-threshold random selection, were used. The comparative results showed the improvements gained by the proposed method over the other methods with which it was compared. Ensuring the QoS in the presence of energy consumption is a challenge for cloud service providers. To address this issue, the authors of [24] proposed an energy-efficient, QoS-aware algorithm for VM consolidation. The proposed algorithm was based on a Markov-chain-based prediction approach in order to measure the load of the hosts, i.e., to identify over-utilized and underutilized hosts. The linear weighted sum approach was used for VM selection and placement during migration. The algorithm targets QoS and energy consumption. Techniques for energy efficiency suffer from the problem of performance degradation. To tackle this issue, thresholding is used to achieve a balance between the two parameters. The authors of [25] proposed an energy-efficient algorithm for VM consolidation. The objectives of the method were energy efficiency and throughput. The workload of a host was considered in order to set thresholds for resource utilization and manage VM migrations. The proposed method performed well in terms of the desired parameters in comparison with other standard methods. In [26], the authors proposed a scheduling algorithm based on the Deep Q-network (DQN) technique. The objectives of the method were the optimization of energy consumption and makespan. The tradeoff between the two parameters was used to optimize the problem and achieve the desired objectives. The dynamic behavior of the proposed algorithm according to different workload requirements was proved with experimental results. The results showed the effectiveness of the proposed algorithm in comparison to other methods. The authors of [27] used bio-inspired heuristics for VM consolidation. A more desirable environment for cloud computing was considered on the basis of a larger capacity margin and a higher fitness value for the VMs and hosts, respectively. The comparative experimental results were shown to validate the performance of the proposed algorithm. The authors of [28] proposed an algorithm that overcame the issues of load balancing and load scheduling. The algorithm worked with precision and privacy. The authors used a hybrid approach that could allocate tasks by using the Modified Canopy Fuzzy C-means Algorithm (MCFCMA). The algorithm allocates tasks to respective resources with the help of PSO. In the proposed technique, the load is based on the selected tasks, the cluster requests the tasks with the help of the MCFCMA, and it schedules these tasks for each VM. The feature value is calculated with the help of the PSO algorithm. The proposed technique minimizes the execution time, cost, and load. A scheduling algorithm that uses the Traveling Salesman Problem (TSP) solution strategy was proposed in [29]. The algorithm first converts the problem into an instance of the TSP and then applies the solution to the problem. The method consists of three phases, i.e., clustering, conversion, and assignment.