# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 7 Performance Evaluation
### Discussion  
To summarize, we were able to describe the core fundamentals of our proposed UAV-assisted Software-Defined Edge Network and task offloading heuristics through a step-by-step examination of the test scenarios. These scenarios focused on two main aspects: task request inter-arrival time and the average speed of the vehicles.  
The first scenario helped us to investigate the capacity of the designed network under three different traffic requirements. Unfortunately, many of the vehicles share the same interfaces, which impacts their available transmission bit rates and leads to low capacity levels. From the second scenario, we observed that vehicle mobility has a big impact on our system since it dynamically modifies the network topology. During the handover, all user-related flows should be modified. Our offloading orchestrator handles  
Figure 11: Impact of average vehicle speed on task delay distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Case \#** & **1/\(\lambda\)** & \(\overline{\mathbf{V}}\) & \(\mathbb{D}\) & **Task Failure Ratio** & **Cloud Ratio** & \(\overline{\mathbf{t}}_{pool}\) \\ \hline Only-Cloud & 2.1 & 8 & 5 & 47.31 & 0.199 & 1 & 0 \\ \hline Only-Cloud & 2.2 & 8 & 20 & 63.66 & 0.282 & 1 & 0 \\ \hline Only-Cloud & 2.3 & 8 & 40 & 71.04 & 0.358 & 1 & 0 \\ \hline AGG-1 & 2.1 & 8 & 5 & 36.63 & 0.153 & 0.042 & 11.54 \\ \hline AGG-1 & 2.2 & 8 & 20 & 49.35 & 0.211 & 0.01 & 12.45 \\ \hline AGG-1 & 2.3 & 8 & 40 & 72.29 & 0.42 & 0.117 & 21.02 \\ \hline AGG-2 & 2.1 & 8 & 5 & 33.22 & 0.146 & 0.03 & 13.11 \\ \hline AGG-2 & 2.2 & 8 & 20 & 47.03 & 0.212 & 0.015 & 11.54 \\ \hline AGG-2 & 2.3 & 8 & 40 & 73.14 & 0.478 & 0.121 & 17.67 \\ \hline ADP & 2.1 & 8 & 5 & 28.71 & 0.137 & 0.107 & 17.31 \\ \hline ADP & 2.2 & 8 & 20 & 41.35 & 0.197 & 0.086 & 17.64 \\ \hline ADP & 2.3 & 8 & 40 & 66.15 & 0.399 & 0.256 & 29.53 \\ \hline Q-OPT & 2.1 & 8 & 5 & 5.51 & 0.055 & 0.146 & 26.27 \\ \hline Q-OPT & 2.2 & 8 & 20 & 10.08 & 0.033 & 0.133 & 24.11 \\ \hline Q-OPT & 2.3 & 8 & 40 & 30.69 & 0.143 & 0.188 & 35.27 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Impact of average vehicle speed on QoS and processing characteristics.  
this job, but reconnection and topology discovery-related delay overheads occur during topology changes.  
The Only-Cloud is sufficient when the request rate is low. However, we clearly observe that MEC-based solutions are necessary in high-load cases due to the bottleneck on the back-haul connection. After investigating the performance of our algorithms, it can be said that ADP outperformed the other heuristic algorithms. This result is related to the network's core problem: it requires effective congestion control methods to handle high levels of traffic load. If we compare AGG-1 and AGG-2 with each other, they have similar objective function results, but the average delay is lower in the more aggressive case, AGG-2, due to the extra queue time in AGG-1.  
Q-OPT appears to outperform the others, especially as the service request rate increases because it can make better decisions in advance while the others make decisions with much fewer data in real-time. Q-OPT highly utilizes cloud server processing from the beginning and thus slows down when the network is congested. In addition, it fails to complete the processing of some tasks on purpose. It does not serve the task owners with their departure time earlier than their task's deadline even when the system is idle and the task can be handled. In the high-load cases, the algorithm also skips some tasks for the sake of a better overall score.