# Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model
## 6 Observations
### Assessment on Placement Cost  
The information shown in Figure 5 and Table 5 clarifies the assessment of the twin fold moth flame algorithm (TF-MFA), which is the methodology provided, in addition to other models, regarding placement cost. This breakdown includes a thorough examination of important placement cost parameters, providing information on how the TF-MFA model performs in comparison to other strategies. The thorough evaluation helps determine how cost-effective the TF-MFA model is when compared to other models that are already in use, and it offers insightful information about the financial aspects of virtual machine deployment strategies in cloud computing settings.  
Figure 5: Portrays the assessment of placement costs for new and current work while allocating load-balancing virtual machines of **(a)** VM = 10, **(b)** VM = 20, **(c)** VM = 30, and **(d)** VM = 40. The subfigures shows the cost dynamics and financial implications of placing these virtual machines in new project creation and operation. Each subfigure represents a VM distribution scenario, showing how placement costs change with load balancing size. This figure helps decision-makers reconcile the distribution of resources and costs for new and current workloads by examining cost trends, possible savings, and effectiveness.  
* Graphical analysis: Figure 5 displays the placement cost of the presented methodology in comparison to other techniques. It illustrates that the presented work consistently earns the lowest placement cost across different variations in VM and block size, thereby fulfilling the theme of minimizing placement cost.
* Percentage comparison: In the case of 40 censuses of blocks and VM = 10, the presented work outperforms prior methods by 64.81%, 61.22%, 59.57%, 60.42%, 65.45%, and 52.5%. This signifies a noteworthy reduction in placement cost compared to alternative procedures.
* Total placement overhead: Table 5 displays a synopsis of the placement overhead for different VM allocations. The presented work reveals a minimal total placement cost, attaining a placement cost of 88.80 when assigning 10 VMs to 1 PM. In comparison, classic approaches show higher placement costs.
* Specific comparisons: The presented method also outperforms constant references like ABC + BA, ACO, CSA, KH, WOGA, and ILWOA when allocating 40 VMs to 1 PM. The presented work achieves reductions in placement cost of 33.62%, 44.49%, 47.92%, 47.54%, 37.17%, and 45.53% compared with these reference models, respectively.  
Overall, the comparison analysis's results show that the twin fold moth flame algorithm, or TF-MFA, consistently has the lowest placement cost among the approaches and dramatically lowers installation expenses. This result highlights how economical the TF-MFA technique is for maximizing resource placement and allocation expenses in the context of cloud computing. The results of this study enhance our comprehension of the financial viability of virtual machine placement techniques and establish TF-MFA as a notable and efficient method for attaining optimal resource allocation in dynamic cloud settings while minimizing installation costs.