# Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model
## 6 Observations
### Assessment of Stability  
The assessment presented in Figure 4, which is measured in Table 4, and the corresponding stability factor values provide a thorough comparison of the advanced technique (TF-MFA) with alternative models, particularly concerning stability influence. This analysis is broken down into a detailed analysis of important stability metrics that show how the TF-MFA model stacks up against other methods. In the context of the proposed evaluation, this thorough assessment is essential for evaluating the TF-MFA model's stability performance and establishing its effectiveness relative to other models already in use. As such, it provides insightful information in the field of stability optimization. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**No. of VM** & **TF-MFA** & **ILWOA** & **WOGA** & **KH** & **CSA** & **ACO** & **ABC + BA** \\ \hline
10 & 0.22457 & 0.28106 & 0.50062 & 0.12212 & 0.11804 & 0.068857 & 0.088531 \\
20 & 0.27208 & 0.18888 & 0.18888 & 0.13604 & 0.078694 & 0.082776 & 0.092612 \\
30 & 0.2045 & 0.15561 & 0.60420 & 0.16100 & 0.068853 & 0.10240 & 0.16550 \\
40 & 0.25816 & 0.21022 & 0.36543 & 0.17131 & 0.092612 & 0.19222 & 0.1332 \\
50 & 0.32367 & 0.3159 & 0.58498 & 0.12869 & 0.1082 & 0.2229 & 0.088531 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stability for various VMs assigned by a PM for the current and suggested models. Figure 3: Compares CTU usage for proposed and current work when a PM distributes (**a**) VM = 10, (**b**) VM = 20, (**c**) VM = 30, (**d**) VM = 40 virtual machines for load balancing. Each subfigure represents a virtual machine distribution scenario with a full quantitative evaluation to improve adaptability. Conversely, the figure shows the present work’s CTU consumption to benchmark the suggested technique. The research covers the same virtual machine distribution spectrum, providing an immediate and insightful comparability. CTU utilization, reaction times, and system throughput are examined to understand CTU use patterns under different scenarios. These subfigures help stakeholders optimize the use of resources and reliability by evaluating energy utilization, and efficiency. * Graphical analysis: In Figure 4, it can be observed that the proposed technique (TF-MFA) guarantees immaculate stability in load balancing while posing fewer risks compared to classic models. The graphical representation emphasizes the downsized risk and enhanced stability provided by TF-MFA. * Probability comparison: The suggested methodology illustrates a significantly lower probability of high-risk circumstances compared to prior models. Particularly at 35 counts of clusters in the 30 th virtual machine, TF-MFA is 82.32%, 81.71%, 81.45%, 81.13%, 75.18%, and 86.79% superior in terms of reduced probability when analogized to ILWOA, WOGA, KH, CSA, ACO, and ABC + BA, respectively. * Stability factor: The stability factor quantifies the stability achieved by each model. The presented method perpetrates a stability factor of 0.2045 when a physical machine (PM) is assigned 30 virtual machines (VM) for a given role. In comparison, the stability factor values for ILWOA, WOGA, KH, CSA, ACO, and ABC + BA are 0.15561, 0.60420, 0.16100, 0.068853, 0.10240, and 0.16550, respectively, TF-MFA portrays a higher stability factor, signifying improved stability performance. The overall results of the comparative analysis highlight how much more stable and successful the suggested method, the twin fold moth flame algorithm (TF-MFA), is than  
Figure 4: Portrays the stability of the proposed methodology over ongoing projects when a physical machine distributes (**a**) VM = 10, (**b**) VM = 20, (**c**) VM = 30, (**d**) VM = 40 virtual machines for load balancing. The approach’s resilience and coherence across diverse virtual machine deployments has been depicted. A physical computer deploys virtual machines for load balancing. This examination evaluates the stability of the system.