## 4 Performance Evaluation
### Experimental Results
#### 4.3.6 Summary of Results  
The simulation results provide important information on how different resource-management methods perform, as shown in Table 5 and Figure 9(a)-e. When it comes to overall system efficiency, the location-based approach excels, especially when it comes to CPU and RAM consumption. The location-based approach outperforms the power-saving algorithm by 0.15%, as seen in Figure 9(a), which also shows the lowest average CPU utilization. It also has the lowest memory utilization, as demonstrated by Figure 9(b), which beats the power-saving strategy by 0.19%. The efficiency of the system can be ascribed to the algorithm's capacity to distribute computer resources geographically, hence reducing latency and optimizing resource allocation, ultimately improving system performance. An intriguing pattern showed that within the first 500 s of the 2000 s simulation, the performance of the power-saving, worst-fit, and best-fit algorithms stabilized, with the exception of the location-based approach. This suggests that in order to effectively distribute workloads and maximize resource efficiency, these algorithms could need a time span of early adjustment. After the first 500 s, in most cases, no changes were observed, with the only exception in the location-based algorithm, where it was affected by the continuous movement of the users. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Best Algorithm for Each Metric** \\ \hline Average CPU utilization (cores) & Location-based algorithm had the lowest core use of the algorithms. It was 0.15\% more efficient from the power-saving algorithm \\ \hline Average Memory utilization (MB) & Location-based algorithm had the lowest memory use of the algorithms, and it was 0.19\% more efficient than the power-saving algorithm \\ \hline Average Disk Utilization (\%) & Best-fit algorithm had the lowest average disk utilization and was 38.1\% more efficient than the power-saving algorithm \\ \hline Average Power Consumption (W) & Location-based algorithm had the lowest power consumption. It was 1.1\% more efficient than the power saving algorithm and 5.08\% more efficient than the worst-fit algorithm \\ \hline Average Latency (ms) & Location-based algorithm had the lowest response time and was 30.82\% more efficient than the other algorithms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of simulation results. Figure 10: _Cont_. As Figure 10c illustrates, the best-fit algorithm turns out to be the most efficient in terms of disk utilization. It is 38.1% more efficient than the power-saving algorithm and shows the lowest average disk utilization. It is especially helpful in situations where there is a significant demand for storage since its better management of storage resources lowers waste and increases the lifespan of servers. The location-based algorithm performs exceptionally well in terms of power usage as well. Its efficiency is 1.1% higher than that of the power-saving algorithm and 5.08% higher than that of the worst-fit method, as shown in Figure 10d. Its capacity to shorten data transmission lengths by emphasizing geographic closeness and make better usage of CPU and memory is largely responsible for this drop in power usage. Additionally, by reducing latency, the location-based algorithm greatly improves user experience. Its response time is 30.82% faster than the other algorithms', as seen in Figure 10e, highlighting its efficacy in dynamically allocating workloads to servers that are physically closer to consumers. The location-based algorithm is now positioned as a top method for controlling edge cloud resources due to its reduced latency and effective use of computing power and resources. Figure 10: Performance results per algorithm for **(a)** CPU cores utilization (Log Scale); **(b)** Memory utilization (Log Scale); **(c)** Disk utilization results; **(d)** Power consumption results (Log Scale); **(e)** Latency results. To sum up, every algorithm exhibits unique benefits based on various measures.