## 5 Performance Evaluation  
To evaluate the efficiency of our proposed FCTO mechanism for MEC-enabled DDSCNs, we used EdgeCloudSim simulator [58] for simulating different scenarios. This simulator extends the CloudSim toolkit [59] by including additional features that allow for modeling the virtualized resources, mobility model, network modeling, and edge orchestrator module to enable accurate simulations of the physical edge infrastructure environment. We used this environment to evaluate how different offloading techniques affect the task failure rate, average service time, and server utilization for different numbers of devices and task sizes. For simulation, we used an augmented reality (AR) application which represents the realistic real-life scenarios. Here, the offloaded tasks of mobile devices are considered as a set of AR applications. For example, a user can offload some pictures to the server by wearing smart glasses for face recognition service. The characteristics of the AR application and its respective values, which are used during simulation, are represented in Table 6. In this simulation, we considered the number of mobile devices to be within 100 and 1000 and the number of SBSs to be 16. During task generation, the system is considered to be in active mode during which the mobile device generates the task. Thereafter, it will be in idle mode for some period. We set 40 s for the active mode and 20 s for the idle mode. For sending the data to the server or receiving the data from the server, we selected the data size 1500 KB for uploading and 25 KB for downloading. The uploaded data size is large compared with the downloaded data because the AR application needs to upload the image in the server and the server responds with a text metadata which requires a smaller data size during downloading. The offloaded task can be either real time or not and it can be determined using delay sensitivity. Moreover, to determine the frequency for sending the task to the edge, the inter-arrival time of the task is used in our simulation. To verify the effectiveness of the FCTO performance, we compared the average task completion time and the average server utilization corresponding to the average task sizes based on three task offloading schemes: local SBS-MEC offloading, workload orchestration based task offloading (WOTO) between the SBS-MEC server with cloud, and our proposed FCTO among SBS-MEC servers. Figure 8a,b represents the results of comparing the performance, where the \(x\)-axes indicate the scalability of task sizes varying from 2 to 20 GI (Giga Instructions), and the \(y\)-axes denote the average task completion time and average server utilization, respectively. From Figure 8a, it can be seen that, when the average task size is small, i.e., 2 GI, then the average task completion time is almost similar for local SBS-MEC offloading and the proposed FCTO scenario. Meanwhile, when the task size is between 2 and 14 GI, the proposed FCTO scheme will require a lower completion time compared with WOTO and local SBS-MEC offloading schemes. This is because, for small and medium task sizes, it is easy to handle the task among SBS-MEC servers, but when the task size becomes large, such as at 16 GI, the WOTO scheme will require lower completion time compared with the other schemes. This is because this scheme forwards the task to the cloud for processing, which has unlimited computing capacity. In this simulation, we considered the number of mobile devices to be 100.