## 2 Related Work  
There are many different types of meta-heuristics that may be used in the search for the best answer to a wide variety of optimization issues [21]. Many meta-heuristic algorithms have recently been effectively used to solve optimization issues. Solving complicated problems with meta-heuristic techniques is appealing because they provide good solutions even for extremely large problems in short periods of time. In this section, we review and represent the related works on load balancing in cloud computing, which are similar and relevant to our work. Related research is grouped around meta-heuristic algorithms inspired by load-balancing approaches that have been developed for cloud computing environments, as well as hybrid optimization algorithms that are based on variations of GA, PSO, and GWO. Effective task scheduling and load distribution across all processing nodes may reduce response, execution, and waiting time, boosting cloud system performance. Several GA-based load-balancing strategies listed in Table 1 have been presented. The I-GA, an improved genetic algorithm is presented to solve the virtual machine placement issue in a cloud data center to increase availability and energy usage [22]. In [23], the author proposed an algorithm for load-balanced task scheduling, job spanning time, and load-balancing genetic algorithm (JLGA) that considers an double-fitness adaptive algorithm. In [24], an optimized protocol based on genetic algorithm is designed for cluster head selection. Due to the high number of retransmissions induced by packet loss, the data transmission latency of some smart meters is considerable. A genetic algorithm is presented to avoid this issue and improve the end-to-end latency in [25]. For scheduling tasks in a cloud environment,  
Figure 2: Container as a Service Model. a Multi-Population Genetic Algorithm (MPGA) that takes load balancing into account is used in [26]. In [27], for multimedia applications, the author offered a novel genetic load-balancing technique that distributes the load across servers and thereby minimizes the response time to the users. Authors in [28] proposed GA-OA (genetic algorithm-based adaptive offloading) for efficient traffic management in an IoT-infrastructure-cloud scenario. Several other GA-based approaches have been proposed for load balancing in the cloud in [29; 30; 31]. The Particle Swarm Optimization is another type of nature-inspired algorithm that has been proposed for resource scheduling and load balancing in cloud systems by many researchers [32; 33; 34]. To allocate resources for the execution of different tasks, [PERSON] and [PERSON] in [32] proposed a modified PSO algorithm that minimizes the makespan, maximizes resource utilization, and at the same time performs load balancing. In [33], [PERSON] et al. proposed a mutation-based PSO algorithm for load balancing. The main idea of this algorithm is to minimize the makespan and improve the fitness function. The experimental result indicates that the proposed algorithm performs better than a normal PSO algorithm. In [34], authors proposed the FIMPSO algorithm for load balancing and energy efficiency. To minimize the search space, a Firefly FF algorithm is proposed and for an enhanced response, the IMPSO algorithm has been proposed. The parameter considered for evaluation were throughput, makespan, execution time, and resource utilization. Authors in [35; 36] introduced a strategy that ensures that tasks that cause VM overload are transferred to comparable VMs in the cloud system in the most efficient manner possible. The proposed optimization model's aim functions are task execution and transfer time minimization. Minimizing transfer time and execution time is their main objective. For independent and non-preemptive tasks in the cloud, a PSO-based static task scheduling approach has been proposed in [37]. In [38], the authors emphasized how considering only execution time can lead to workload imbalance in a virtual machine.