# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 4 Performance Evaluation
### Comparison of EDC Deployment Strategies  
First, a study about latency and workload was conducted using the distributed methodology to ensure that these metrics are not adversely affected by the proposed approach. Table 3 compares the latency and workload results between the serial version and the distributed Spark version for different EDC configurations (20, 30, and 40 EDCs), presenting the latency and workload, along with their standard deviations, for both versions. Additionally, these results are shown in Figure 4 to graphically illustrate the trend of both metrics based on the number of EDCs deployed across the city of Shanghai. \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Node Type** & **Description** \\ \hline Single Primary Node & \begin{tabular}{l} Also known as the _master_ node. It coordinates the execution of tasks \\ across the cluster. \\ \end{tabular} \\ \hline Task Nodes & \
\begin{tabular}{l} These nodes are responsible for executing portions of the workload \\ assigned by the _master_ node. \\ \end{tabular} \\ \hline Central Node &
\begin{tabular}{l} This node manages the administration of the Hadoop Distributed File \\ System (HDFS) operations, such as read and write tasks. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of node types used during Spark execution. Figure 4: Performance evaluation results based on the selected metrics: (**a**) Latency (km); (**b**) Workload (min). Figure 4a shows that the latency values are almost identical between the serial and Spark versions, with minor differences in their standard deviations, indicating that both methods deliver similar performance in terms of latency. On the other hand, as shown in Figure 4b, there is a slight variation in the workload between the two versions, with the Spark version generally showing a higher workload, particularly for the 20 EDCs configuration. This suggests that the distributed method may handle a slightly higher load, although the difference decreases as the number of EDCs increases. Figure 5 illustrates the deployment of different EDC configurations across the city of Shanghai, providing coverage for the 3000 base stations included in the dataset. This visual representation highlights how various configurations of EDCs are distributed throughout the city to ensure optimal coverage and connectivity. Figure 5 shows that both the serial and distributed strategies for EDC placement yield comparable results. This similarity is expected, given that both approaches aim to effectively manage and balance the load among the base stations. The distribution patterns demonstrate that, regardless of the methodology used, the coverage and performance metrics achieved are consistently aligned. However, as it will be shown in the following sections, the distributed methodology offers better performance in terms of execution times. Thus, we confirm that both strategies are capable of providing similar coverage and handling the demands of the extensive network effectively, with the distributed approach providing an advantage in execution efficiency. Figure 6 offers a comprehensive and detailed analysis of the performance characteristics of the EDC placement algorithm implemented within the Apache Spark framework, elucidating how the algorithm behaves regarding the number of worker nodes and the size of the datasets. These visualizations not only demonstrate the algorithm's effectiveness in reducing execution time but also provide critical insights into its scalability, efficiency, and overall performance in a distributed computing environment, making them a valuable  
Figure 5: EDC deployments with base stations grouped based on the number of EDCs, using the serial version (**a**–**c**) and the proposed methodology (**d**–**f**).