# Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model
## 7 Conclusions and Future Directions  
The development of a virtual machine placement strategy designed for utilization over wide area network (WAN) links involves the crucial aspect of offline VM selection. The primary objective of offline VM selection is to carefully choose one or more potential virtual machines for placement, ultimately mitigating resource demands on the hosts under consideration. This task is achieved through the implementation of the twin fold moth flame algorithm (TF-MFA) model, which is built upon three key goals: optimizing computing time unit (CTU) utilization, ensuring system stability, and minimizing placement costs. By addressing these goals, the TF-MFA model offers an effective and comprehensive solution for the strategic placement of virtual machines, particularly tailored for scenarios involving WAN links in cloud computing environments. The presented model's effectiveness was examined by calculating placement costs, computation times, and stability assessment. The most significant result of the presented work was 3.23%, 8.72%, 10.09%, 27.82%, 6.62%, and 9.61% for 20 count data of nodes for the artificial bee colony-bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Levy-based whale optimization algorithm, as ambiguously portrayed in Figures 3-5. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**VM at 10 Counts** & & & & & & & \\ \hline
**Behaviors** & **TF-MFA** & **IILWOA** & **WOGA** & **KH** & **CSA** & **ACO** & **ABC + BA** \\ \hline Best & 1786.4 & 2110.7 & 2110.5 & 2754.1 & 1985.3 & 2042.5 & 1996.3 \\ \hline Worst & 3421.4 & 3607.4 & 3807 & 4983.4 & 4313.8 & 3685 & 3734 \\ \hline Mean & 2550.7 & 2950.6 & 2935.1 & 3563.7 & 3069.4 & 2908.6 & 2846.4 \\ \hline Median & 2497.5 & 3042.2 & 2911.4 & 3259.1 & 2989.4 & 2953.4 & 2827.6 \\ \hline STD & 715.48 & 732.15 & 746.18 & 1030.7 & 989.64 & 698.48 & 748.99 \\ \hline \hline \multicolumn{10}{l}{**VM at 20 counts**} \\ \hline Behaviors & TF-MFA & **IILWOA** & **WOGA** & **KH** & **CSA** & **ACO** & **ABC + BA** \\ \hline Best & 1982.4 & 2193.3 & 2123.1 & 2746.8 & 2205.1 & 2171.9 & 2048.6 \\ \hline Worst & 3639.5 & 3657.7 & 4012.3 & 3798.8 & 3733.5 & 3612.8 & 3468.4 \\ \hline Mean & 2750.5 & 2893.7 & 3020.4 & 3268.1 & 2969 & 2835.7 & 2763 \\ \hline Median & 2690.1 & 2862.4 & 2973.2 & 3263.4 & 2968.6 & 2779.1 & 2767.6 \\ \hline STD & 734.3 & 655.5 & 785.28 & 431.17 & 650.1 & 624.92 & 611.02 \\ \hline \hline \multicolumn{10}{l}{**VM at 30 counts**} \\ \hline Behaviors & TF-MFA & **IILWOA** & **WOGA** & **KH** & **CSA** & **ACO** & **ABC + BA** \\ \hline Best & 1685.1 & 2003.4 & 2168.6 & 2538.5 & 2092.3 & 2087.6 & 1946.5 \\ \hline Worst & 3208.2 & 3625.8 & 4235.3 & 4101.8 & 3841.8 & 3648.9 & 3469.5 \\ \hline Mean & 2539.3 & 2898.2 & 3054 & 3396 & 2979.7 & 2992 & 2697.6 \\ \hline Median & 2632 & 2981.9 & 2906 & 3467.8 & 2992.3 & 3115.7 & 2687.2 \\ \hline STD & 694.09 & 713.6 & 901.66 & 695.84 & 770.09 & 757.58 & 648.34 \\ \hline \multicolumn{10}{l}{**VM at 40 counts**} \\ \hline Behaviors & TF-MFA & **IILWOA** & **WOGA** & **KH** & **CSA** & **ACO** & **ABC + BA** \\ \hline Best & 1763.9 & 2200.1 & 2355.4 & 2166.4 & 2046.8 & 1985.7 & 1913.5 \\ \hline Worst & 3450.1 & 3890.3 & 4448.4 & 4691.1 & 3833.1 & 3607.8 & 3600.9 \\ \hline Mean & 2661.8 & 2989.8 & 3203.8 & 3574.6 & 2954.5 & 2781.9 & 2748.5 \\ \hline Median & 2716.7 & 2934.5 & 3005.6 & 3720.5 & 2968.9 & 2767 & 2739.8 \\ \hline STD & 700.33 & 760.01 & 942.95 & 1104.3 & 755.04 & 701.04 & 733.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Probabilistic assessment of a PM’s dynamic VM allocation for load balancing purposes. The swift progress of computer science and technology is significantly influencing many aspects of our everyday lives and work settings. Looking ahead, we believe that combining state-of-the-art applied sciences--most notably, artificial intelligence and big data [30]--will be essential.