## 6 Experimental Evaluation
### Experiment II: Service Allocation
#### 6.2.1 EdgeSimPy Simulator  
EdgeSimPy [33] is a simple and intuitive Python-based framework to model and simulate resource management policies in edge computing environments. It offers a modular architecture for edge servers, network devices, and applications, enabling the modeling of the entire lifecycle of edge applications with real data traces.  
In addition, EdgeSimPy includes specific components for MEC simulation, allowing detailed evaluation of architectures and resource allocation algorithms [33].  
Figure 8 illustrates the components of EdgeSimpy used to simulate the Ubunye scenarios, showcasing an example scenario with the interconnections of the main functional abstractions. Each base transceiver station (BTS) is represented at the center of a hexagon, indicating its coverage area. The letter 'S' represents the services available to users, while the 'L' lines depict the network connections between BTSs, forming the network topology. The legend also describes the nodes at the network's edge.  
Figure 8: Ubunye service components using the EdgeSimPy simulator.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Configuration** \\ \hline Number of Users & [30; 150] \\ \hline Number of BTS & 16 \\ \hline Number of Network Switches & 16 \\ \hline Number of Network Links & 33 \\ \hline Number of Applications & 6 \\ \hline Number of Services & 6 \\ \hline Number of Edge Nodes & 6 \\ \hline Number of CPUs (each Edge Node) & 8 \\ \hline Memory Capacity (each Edge Node) & 16 MBytes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Service allocation simulation parameters.  
In Figure 8, the user component models mobile users who request services through the service components (S1, S2, and S3). Each service is linked to a mobile application, represented by the application component. Users labeled User 1 and User 2 change positions throughout the simulation and are always associated with a BTS, depicted by the BaseStation component (the antennas in the figure).  
Each BTS connects to a router (network switch component), which serves as the network element from which the topology interconnections (network link component) originate, represented in Figure 8 by lines L1 to L8. Additionally, the edge nodes associated with the ERBs and routers are shown as ES1, ES2, and ES3. These edge nodes are responsible for hosting the network services requested by applications. A resource allocation algorithm will orchestrate this process, establishing the criteria needed to meet QoS and QoE requirements.  
This work established a stopping criterion of 1440 min (24 h) for generating meaningful information. Network traffic varies significantly over 24 h because users access the Internet differently throughout the day, whether during business hours, evening streaming, or late-night browsing. These shifts in usage, influenced by daily schedules and varying time zones, create fluctuations in network load that cannot be fully captured in a shorter simulation. This approach is beneficial for evaluating resilience and reliability, which are essential when managing peak loads and ensuring consistent service delivery [34].