# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 5 Experiment Results and Analysis
### Experiment Results  
The experimental process is as follows. First, map the user locations at the current time onto the map. Then, calculate the optimal placement locations and numbers of edge servers at the current time according to the ISO algorithm. As time passes and user locations change, recalculate the optimal placement locations and numbers of edge servers using the ISO algorithm and the dynamic edge-server placement algorithm. Next, calculate the \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Retrieved Value** \\ \hline Channel bandwidth \(w\) & \(10\,\mathrm{MHz}\) \\ User transmission power \(P\) & \(0.2\,\mathrm{W}\) \\ Path loss factor \(\alpha\) & \(10\) \\ Channel gain between user and edge server \(h_{1}\) & \(0.1\) \\ Noise power \(N_{0}\) & \(1.6\times 10^{-11}\) \\ Channel gain between user and cloud server \(h_{2}\) & \(0.98\) \\ Distance from user to cloud server \(l\) & \(1\times 10^{6}\,\mathrm{m}\) \\ The number of initial static servers \(N\) & \(10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiment parameters. minimum placement cost between this moment and the previous moment according to the minimum placement-cost algorithm, and set the edge servers that have not moved as static servers. Figure 8 shows the cost values corresponding to different time points for various edge-server computing abilities. Clearly, the cost vs. time curves exhibit a decreasing trend as the edge-server computational ability increases. This is because the required number of edge servers decreases with increasing computational ability, which decreases the cost. Further, as the edge-server computational ability increases, the cost values grow closer. This is because, for low edge-server computing ability, some users within the edge-server coverage area are unable to offload their tasks to the server, and therefore, additional servers are required to fulfill the demand. However, as the computing ability increases, the number of additional servers required to compensate for a lack of computing ability decreases. Therefore, as the edge-server computational ability increases, the cost value gradually converges to a stable value. Figure 9 shows the average user delay corresponding to different time points for various edge-server computational abilities. Clearly, the average user latency gradually decreases with increasing edge-server computational ability. This is because, when the edge-server computational ability is higher, more users can offload their tasks to the edge server for processing and not to the cloud; thus, the latency is reduced. Figure 8: Impact of computing ability on cost. Figure 9: Impact of computing ability on delay. Figure 10 shows the cost values corresponding to different time points for various edge-server coverage values. Clearly, the costs corresponding to different time points exhibit a decreasing trend as the edge-server coverage increases. This is because an increase in coverage reduces the need for dynamic servers and, thus, the cost. Figure 11 shows the average user delay corresponding to different time points for various edge-server coverage values. Clearly, the average user delay gradually decreases with increasing edge-server coverage. This is because increased edge-server coverage facilitates coverage of more users, who can offload their tasks to the edge servers for processing without relying on the cloud; thus, the latency is reduced. Figure 12 shows the cost values corresponding to different time points for various values of the maximum number of tasks randomly generated by a user. Clearly, as the number of tasks increases, the cost vs. time curves exhibit an increasing trend. This is because, for increasing user tasks and a certain edge-server computational power, more edge servers are required to process the tasks, and thus, the cost is increased. Figure 10: Impact of coverage on cost.