## 5 Simulation Results  
Cloud computing is a distributed infrastructure where data, computation, storage, and applications are dispersed locally between data-generating devices and the cloud. The concept of fog computing exists in both cloud systems and big data structures, where the problems of accessing information are increased. A distributed fog infrastructure is replacing a centralized cloud computing architecture. Both fog computing and cloud computing provide users with storage, application, and data. However, the most important and effective point in this field is the challenge of energy consumption in data centers and reducing the response time of machines in fog computing. Most of the workload in this form of network is the responsibility of data centers; due to the nature of the devices, which are not similar to the cloud computing environment, it has the same expectations as human users, so the control and management of the device in cloud computing is the responsibility of the data centers. Based on this, the results of the proposed algorithm will be discussed in this section. The experimental environment is performed on Matlab R2017b on a 2 GHz processor Intel(r) Core(tm) i7 laptop, with 8 GB memory. Figure 4: Block diagram of the ISOA. To assess the suggested technique, first, the results were tested using the suggested ISOA, and then it was validated through comparison with some published methods, including MRC [17], IoTSim-Osmosis (IOT/OS) [32], IoTSim-Stream (IOT/ST) [33], and the standard SOA-based technique. Here, the methods that have not used metaheuristics in their work have been extended by metaheuristics based on SOA to provide a fair comparison. To test the proposed solution, the ISOA parameters are defined in Table 1.  
The succeeding graphs related to the evaluation of the proposed method based on average execution time, makespan, and virtual machine computing cost. Figure 5 displays the execution time for all comparative algorithms. It can be seen from Figure 5 that the execution time for the comparative methods is displayed in two hundred iterations, and although all algorithms are descending, the proposed ISOA was able to achieve the lowest possible value in the last iteration. Figure 6 shows the makespan of all the studied techniques. Figure 6 shows the makespan of all studied techniques. It can be seen from Figure 6 that the amount of makespan for all algorithms is displayed in two hundred iterations, and the amount of makespan in the proposed algorithm is significantly decreased in the last iteration compared to the compared algorithm. Figure 7 shows the virtual machine computing cost value for all comparative algorithms. As can be seen from Figure 7, in this graph, the virtual machine computing cost for all methods is shown in 200 iterations, and the proposed algorithm was able to achieve the lowest cost with a much lower amount in the last iteration. Figure 8 displays the network cost for all comparative algorithms. It can be seen that the network cost is the same even if the VM number has changed. This is because, given the same workload, the data size produces the same network latency. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(u\) & 1 \\ \(v\) & 0.002 \\ Iteration number & 200 and 1000 \\ population & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Set parameter of the proposed ISOA in this study. Figure 5: Execution time for the comparative algorithms. Figure 8: Network cost for all comparative algorithms. Figure 6: Makespan for the investigated methods. Figure 7: The amount of virtual machine computing cost for the comparative algorithms. Based on the results of all three-convergence analyses, several points can be pointed out. All methods have gone through an acceptable downward path to reach the solution. The proposed ISOA moves towards the optimum at a high speed and has a high convergence speed.