## 4 Performance Evaluation  
### Experimental Setup  
The experimental setting for assessing the efficacy of resource management algorithms in edge cloud environments is specifically built to guarantee the ability to replicate results, maintain uniformity, and accurately represent real-world conditions in the simulated environment. We employ the EdgeSimPy simulation tool to construct a virtualized edge cloud environment that faithfully replicates the features and dynamics of actual edge computing scenarios. Our experimental simulation scenario, where entities interact with one another and the environment is affected by resource management decisions, is shown in Figure 3. EdgeSimPy requires an input file (dataset) that defines the simulated scenario infrastructure and entities before the simulation begins and receives statistics and metrics as defined in the next section throughout the duration of the simulation. The experimental scenario in Figure 3 consists of a 4 \(\times\) 4 hexagonal grid layout using the map model proposed in [26], where the servers in the designated area are, we assume, deployed in an urban zone divided into hexagonal cells, as typical in mobile cellular networks. Each cell has its own unique coordinates and has direct communication with the cells immediately adjacent to it. Because the simulated area is uniformly covered by the hexagonal grid map, we are able to assess how well resource management algorithms function in various geographic locations. Every grid cell has a base station that is connected to the internet, which makes it easier for users and edge servers to communicate and relocate workload to an adjacent server. The base stations are arranged in a way that guarantees communication with directly connected grid cells. In addition, every base station is equipped with a 10 Gbps network capacity and a 1 s latency. This allows users and edge servers to communicate in a realistic manner while taking latency and bandwidth limitations into account. The infrastructure consists of six edge servers total, as shown in Table 4, dispersed over the grid layout with the promise that two edge servers cannot be assigned to the same grid cell. Two NVIDIA Jetson TX2 models (servers 5 and 6) and four Raspberry Pi 4 (servers 1-4) models are used as our edge servers. These edge servers are dispersed over the network, as seen in Figure 4. Every grid cell denotes a specific geographic region that contains an edge server. Six users are also included in our simulation scenario; each of them is assigned randomly to a certain grid cell. The power model and computational capacity of each edge server are also defined in Table 4. The NVIDIA Jetson TX2 models have a quad-core ARM Cortex-A57 processor with a CUDA-enabled GPU, 8 GB of RAM, and a 32 GB disk capacity with a minimum power consumption of 7.5 W and 15 W at a maximum workload. Meanwhile, the Raspberry Pi 4 models have 8 GB of RAM, a quad-core ARM Cortex-A72 processor, a 32 GB disk capacity, and a power consumption of 2.56 W at minimum workload and 7.3 W at maximum (based on [27]). We are able to precisely model the energy consumption of each edge server under \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Servers** & **CPU Cores** & **Memory (MB)** & **Disk (MB)** & \
\begin{tabular}{c} **Min Power** \\ **Consumption (Watts)** \\ \end{tabular} &
\begin{tabular}{c} **Max Power** \\ **Consumption (Watts)** \\ \end{tabular} \\ \hline Edge server 1 (Raspberry Pi) & 4 & 8192 & 32,768 & 2.56 & 7.3 \\ \hline Edge server 2 (Raspberry Pi) & 4 & 8192 & 32,768 & 2.56 & 7.3 \\ \hline Edge server 3 (Raspberry Pi) & 4 & 8192 & 32,768 & 2.56 & 7.3 \\ \hline Edge server 4 (Raspberry Pi) & 4 & 8192 & 32,768 & 2.56 & 7.3 \\ \hline Edge server 5 (Jetson TX2) & 4 & 8192 & 32,768 & 7.5 & 15 \\ \hline Edge server 6 (Jetson TX2) & 4 & 8192 & 32,768 & 7.5 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Edge server’s specifications. Figure 4: Experimental setup use case.