## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** &
\begin{tabular}{c} **Author** \\ **Name** \\ \end{tabular} & **Year** & \multicolumn{3}{c}{**Pros and Cons**} & \\ \hline
[35] & \begin{tabular}{c} [PERSON] \\ and [PERSON] \\ \end{tabular} & 2019 & \begin{tabular}{c} \(\bullet\) \\ \(\bullet\) \\ \end{tabular} & \begin{tabular}{c} Well-organized resource usage. \\ Better organized than RR, throttled, and ESCE \\ LB algorithms. \\ \end{tabular} & \
\begin{tabular}{c} \(\bullet\) \\ \(\bullet\) \\ \end{tabular} &
\begin{tabular}{c} The proposed algorithm lacks live \\ migration of virtual machines. \\ The proposed algorithm lacks \\ auto-scaling strategies. \\ \end{tabular} \\ \hline
[35] & \begin{tabular}{c} [PERSON]. \\ [PERSON] \\ and [PERSON] \\ \end{tabular} & 2019 & \begin{tabular}{c} \(\bullet\) \\ \(\bullet\) \\ \end{tabular} & \
\begin{tabular}{c} Various routing schemes are in sequence to \\ decrease the processing time. \\ The data center is used to execute the task based \\ on the limited distance of the track. \\ \end{tabular} &
\begin{tabular}{c} The proposed algorithm lacks \\ improvement in minimal and maximal \\ processing time. \\ \end{tabular} \\ \hline
[36] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2019 & \
\begin{tabular}{c} This research paper tests the efficiency of the three \\ current service broker policies with a strategy of \\ sustained load leveling. \\ \end{tabular} &
\begin{tabular}{c} The research paper lacks analysis of the \\ closest data center (CDC) and \\ reconfigures dynamically with load \\ policy. \\ \end{tabular} \\ \hline
[37] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2018 & \
\begin{tabular}{c} The proposed algorithm distributes tasks effectively \\ among virtual machines based on existing load, \\ priority, memory use, and state of the virtual \\ machines. \\ \end{tabular} &
\begin{tabular}{c} The proposed algorithm is not providing \\ user requirements among the virtual \\ machines based on the reliability and use \\ of the processor. \\ \end{tabular} \\ \hline
[38] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2018 & \begin{tabular}{c} \(\bullet\) \\ \(\bullet\) \\ \end{tabular} & \
\begin{tabular}{c} Job scheduling is about reducing the makespan. \\ The effectiveness of the proposed algorithm is \\ significantly expanded. \\ \end{tabular} &
\begin{tabular}{c} The proposed algorithm has a lack of \\ demand for increasingly more jobs. \\ \end{tabular} \\ \hline
[39] & \begin{tabular}{c} [PERSON] and \\ and [PERSON] \\ \end{tabular} & 2016 & \
\begin{tabular}{c} Enhanced RR is proposed, and findings demonstrate \\ that it outperforms other existing algorithms in terms \\ of average request service time. \\ \end{tabular} &
\begin{tabular}{c} Lack of improvement in active monitoring \\ and throttled LB algorithm. \\ \end{tabular} \\ \hline
[40] & \begin{tabular}{c} [PERSON] and \\ [PERSON] \\ \end{tabular} & 2015 & \
\begin{tabular}{c} The throttled algorithm decreases response time, data \\ center request servicing time, and cost. \\ \end{tabular} &
\begin{tabular}{c} The throttled algorithm lacks virtual \\ machines that possess various hardware \\ configurations. \\ \end{tabular} \\ \hline
[41] & \begin{tabular}{c} [PERSON] \\ et al. \\ \end{tabular} & 2015 & \
\begin{tabular}{c} The proposed algorithm is providing the closest data \\ center and maximize response time functions with \\ low processing times effectively. \\ \end{tabular} &
\begin{tabular}{c} The proposed algorithm is not providing \\ data center configurations using the \\ different broker policies. \\ \end{tabular} \\ \hline
[42] & \begin{tabular}{c} [PERSON] \\ and [PERSON] \\ \end{tabular} & 2015 & \
\begin{tabular}{c} The proposed algorithm has service broker \\ approaches that decrease response time and overall \\ data center costs. \\ \end{tabular} &
\begin{tabular}{c} The proposed algorithm is not providing \\ data center configurations using the \\ different broker policies. \\ \end{tabular} \\ \hline
[43] & \begin{tabular}{c} [PERSON] \\ et al. \\ \end{tabular} & 2020 & \
\begin{tabular}{c} The cloud computing key benefit is that consumers \\ are liberated from fears about learning simple \\ instrumentality that is sensitive to inquiries. \\ \end{tabular} &
\begin{tabular}{c} The allocation of resources, the \\ availability of resources that suit \\ requirements, and a lot of cloud-faced box \\ management problems. \\ \end{tabular} \\ \hline
[44] & \begin{tabular}{c} [PERSON] \\ et al. \\ \end{tabular} & 2020 & \
\begin{tabular}{c} The researcher provides the communication model \\ with the CloudAnalyst platform. \\ \end{tabular} &
\begin{tabular}{c} The communication model for CloudSim \\ is restricted. \\ \end{tabular} \\ \hline
[45] & \begin{tabular}{c} [PERSON] and \\ [PERSON]- \\ hendu \\ \end{tabular} & 2020 & \
\begin{tabular}{c} The novel broker policy reduces overall costs, \\ response time, and processing. \\ \end{tabular} &
\begin{tabular}{c} The novel broker policy is not using \\ optimized methods for cost and response \\ time. \\ \end{tabular} \\ \hline
[46] & \begin{tabular}{c} [PERSON] \\ and [PERSON] \\ \end{tabular} & 2020 & \
\begin{tabular}{c} The water wave algorithm performs better \\ concerning total response time. \\ \end{tabular} &
\begin{tabular}{c} The water wave algorithm has delayed \\ various routing policies. \\ \end{tabular} \\ \hline
[47] & \begin{tabular}{c} [PERSON] \\ \end{tabular} & 2016 & \
\begin{tabular}{c} The cloud analyst is a convenient-to-use GUI for \\ establishing and viewing the results of cloud \\ computing experimentation of all varieties. \\ \end{tabular} &
\begin{tabular}{c} The cloud analyst has security challenges. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}