## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & \
\begin{tabular}{c} **Author** \\ **Name** \\ \end{tabular} & **Year** &
\begin{tabular}{c} **Pros and Cons** \\ \end{tabular} \\ \hline
[24] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2016 & \
\begin{tabular}{c} Based on their priority, status, and memory use, the \\ suggested algorithm distributes the load across the \\ virtual machines in an appropriate manner. \\ \end{tabular} &
\begin{tabular}{c} The proposed approach ignores the \\ virtual machine’s energy awareness and \\ reliability. \\ \end{tabular} \\ \hline
[25] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2015 & \
\begin{tabular}{c} • Effectively, the response time has enhanced. \\ \(\bullet\) \\ \end{tabular} &
\begin{tabular}{c} The suggested algorithm’s fundamental \\ flaw is that it checks the availability of all \\ virtual machines every time it assigns a \\ new load. As a result, request allocation \\ takes longer, resulting in a longer \\ response time. \\ \end{tabular} \\ \hline
[26] & \begin{tabular}{c} [PERSON] \\ et al. \\ \end{tabular} & 2020 & \
\begin{tabular}{c} • The scheduling system lowers the makespan \\ and expense of operation. \\ Scheduling also increases the availability of \\ resources and device scalability for cloud \\ providers. \\ \end{tabular} &
\begin{tabular}{c} • Lack of task scheduling in the \\ heterogeneous cloud environment. \\ Lack of understanding of the various \\ reliability rates. \\ \end{tabular} \\ \hline
[27] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2016 & \
\begin{tabular}{c} The proposed strategy suggested is ideally suited to \\ cloud surroundings. \\ \end{tabular} &
\begin{tabular}{c} Lack of information concerning the \\ overloading of virtual machines and data \\ centers in advance. \\ \end{tabular} \\ \hline
[28] & \begin{tabular}{c} [PERSON] \\ and [PERSON] [PERSON] \\ \end{tabular} & 2015 & \
\begin{tabular}{c} • Analysis of various simulators used for cloud \\ computing. \\ The emulator has features that distinguish it \\ from others. \\ \end{tabular} &
\begin{tabular}{c} CDOSim, TechCloud, DCSim, and \\ GroudSim are not providing the platform, \\ networking, simulation type, or \\ open-source availability. \\ \end{tabular} \\ \hline
[29] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2015 & \
\begin{tabular}{c} • GUI Assistance, underneath the programming \\ language. \\ CloudSim, SPECI, and GreenCloud are \\ platform-supported simulators. \\ \end{tabular} &
\begin{tabular}{c} • Lack of choosing a suitable simulator \\ according to user needs. \\ GroudSim is not providing the \\ platform, networking, simulation \\ type, or open-source availability. \\ \end{tabular} \\ \hline
[30] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2020 & \
\begin{tabular}{c} Multi-agent deep reinforcement learning to dynamic \\ resource allocation is used to decrease the issue of \\ cloud service. \\ \end{tabular} &
\begin{tabular}{c} The proposed solution does not include a \\ dynamic approach based on the virtual \\ machine’s cost analysis. \\ \end{tabular} \\ \hline
[31] & \begin{tabular}{c} [PERSON] \\ [PERSON] et al. \\ \end{tabular} & 2020 & \
\begin{tabular}{c} The proposed policy increased the overall execution \\ time and speed of resource usage. \\ \end{tabular} &
\begin{tabular}{c} The proposed policy has the issue of \\ increased LB issues related to power \\ consumption. \\ \end{tabular} \\ \hline
[32] & \begin{tabular}{c} [PERSON] \\ and [PERSON] \\ \end{tabular} & 2020 & \
\begin{tabular}{c} • Give optimal response time and DCPT. \\ When compared to the existing RR and evenly \\ distributed execution technique, throttled takes \\ less time. \\ \end{tabular} &
\begin{tabular}{c} • Failure to validate which algorithm \\ is ideal. \\ The response time provided by these \\ algorithms is not optimal. \\ \end{tabular} \\ \hline
[33] & \begin{tabular}{c} [PERSON] \\ and [PERSON] \\ \end{tabular} & 2019 & \
\begin{tabular}{c} • RR has the maximum response time. \\ The throttled algorithm is organized in nature. \\ ESCE is easy to deploy. \\ \end{tabular} &
\begin{tabular}{c} • Throttled is substantially lower. \\ RR does not produce an extra \\ effective outcome. \\ \end{tabular} \\ \hline
[34] & \begin{tabular}{c} Al- \\ [PERSON] \\ and [PERSON] \\ \end{tabular} & 2019 & \
\begin{tabular}{c} • The proposed algorithm can accomplish better \\ changes in performance. \\ It can also work in a user-oriented approach. \\ \end{tabular} &
\begin{tabular}{c} • Lack of security ability and security \\ criteria for using apps. \\ The proposed algorithm lacks \\ consumer expenses and contributor \\ income. \\ \end{tabular} \\ \hline
[34] & \begin{tabular}{c} [PERSON] \\ and [PERSON] \\ \end{tabular} & 2019 & \
\begin{tabular}{c} • The proposed algorithm can accomplish better \\ changes in performance. \\ It can also work in a user-oriented approach. \\ \end{tabular} &
\begin{tabular}{c} • Lack of security ability and security \\ criteria for using apps. \\ The proposed algorithm lacks \\ consumer expenses and contributor \\ income. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}