## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author(s)** & **Method** & **Objective of Work** & **Remark** \\ \hline
[22] & GA & Efficient virtual machine placement in cloud data center & Considered only memory and CPU. Other aspect of energy consumption with large problem needs to be considered. \\ \hline
[23] & GA & Scheduling of tasks to ensure the shortest possible makespan and load balancing. & Other static parameters in GA, such as population scale, iterations, crossover, and mutation operator, could potentially exert a negative influence on the rate of convergence and the quality of the global optimal solution. \\ \hline
[24] & GA & The Genetic Algorithm-based Optimized Clustering (GAOC) technique was developed specifically for the purpose of optimizing CH selection. & Work could be extended for the shifting sink situation in order to achieve higher levels of performance. \\ \hline
[25] & GA & GA-based optimization approach was used for load-balanced path & Other constraints such as energy, resource usage can be considered for further improvement. \\ \hline
[26] & GA & The Multi-Population Genetic Algorithm, or MPGA, is a scheduling method for tasks that takes load balance into consideration. & Coexistence of independent and related tasks in practical cloud environment. \\ \hline
[27] & GA & Technique for load balancing used in multimedia applications, with the goal of increasing performance by dispersing the workload among multiple servers & In the future, one of the most important tasks for our study area will be to demonstrate the usefulness and practicality of SDN in blockchain. \\ \hline
[28] & GA & GA-OA in an accompanying that stands for genetic algorithm-based adaptive offloading. It is used for optimal traffic handling in environments involving IoT infrastructure and the cloud. & It is possible to use an adaptive offloading approach that has been strengthened with intelligent learning algorithms in order to extend the flexibility and scalability features present in a heterogeneous network. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of different Meta-heuristic Load-Balancing Approaches Using GA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author(s)** & **Method** & **Objective of Work** & **Remark** \\ \hline
[32] & PSO & A modified PSO algorithm that minimizes the makespan, maximizes resource utilization, and performs load balancing & Work can be extended to improve the quality of service \\ \hline
[33] & PSO & Mutation-based PSO for load balancing in a cloud data center & Work can be extended by considering parameters such as waiting time, throughput, etc. \\ \hline
[34] & PSO & FIMPSO algorithm for load balancing and energy efficiency & Proposed FIMPSO method can be improved to make use of data deduplication \\ \hline
[56] & PSO & Tasks are migrated from overloaded VM to under-loaded VM. The proposed PSO-based algorithm minimizes task execution and transfer time & Work can be extended by considering execution time, makespan, etc. \\ \hline
[7] & PSO & PSO-based static task scheduling algorithm in which it is assumed that the tasks are non-preemptive and independent of each other. & Work can be increased to accommodate workflow applications while also taking into account other quality of service parameters such as fault tolerance and cost minimization. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Review of different Meta-heuristic Load Balancing Approaches Using PSO.