# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 7 Performance Evaluation
### Experimental Results
#### 7.3.1 Impact of Task Inter-Arrival Time
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Case ID** & **1/A** & \(\overline{V}\) & \(\mathbb{D}\) & **Task Failure Ratio** & **Cloud Ratio** & \(\overline{t}_{pool}\) \\ \hline Only-Cloud & 1.1 & 5 & 20 & 85.96 & 0.524 & 1 & 0 \\ \hline Only-Cloud & 1.2 & 10 & 20 & 20.36 & 0.144 & 1 & 0 \\ \hline Only-Cloud & 1.3 & 15 & 20 & 9.36 & 0.083 & 1 & 0 \\ \hline AGG-1 & 1.1 & 5 & 20 & 67.29 & 0.396 & 0.384 & 24.89 \\ \hline AGG-1 & 1.2 & 10 & 20 & 30.84 & 0.193 & 0.024 & 11.29 \\ \hline AGG-1 & 1.3 & 15 & 20 & 15.41 & 0.088 & 0.012 & 5.71 \\ AGG-2 & 1.1 & 5 & 20 & 66.43 & 0.405 & 0.377 & 22.13 \\ \hline AGG-2 & 1.2 & 10 & 20 & 29.67 & 0.201 & 0.029 & 11.6 \\ \hline AGG-2 & 1.3 & 15 & 20 & 16.12 & 0.095 & 0.021 & 8.22 \\ \hline ADP & 1.1 & 5 & 20 & 59.22 & 0.339 & 0.517 & 28.14 \\ \hline ADP & 1.2 & 10 & 20 & 28.9 & 0.19 & 0.123 & 19.57 \\ \hline ADP & 1.3 & 15 & 20 & 11.23 & 0.074 & 0.033 & 10.67 \\ \hline Q-OPT & 1.1 & 5 & 20 & 36.56 & 0.237 & 0.227 & 45.46 \\ \hline Q-OPT & 1.2 & 10 & 20 & 6.71 & 0.096 & 0.218 & 17.65 \\ \hline Q-OPT & 1.3 & 15 & 20 & 6.1 & 0.093 & 0.388 & 10.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Impact of task inter-arrival time on QoS and processing characteristics. Figure 7: Impact of task inter-arrival time on the objective function \(\mathbb{D}\). 2. Impact of Task Inter-Arrival Time on Task Processing Characteristics  
From Table 6, we observe that AGGs achieve lower average pool time \(\overline{t}_{pool}\) resulting from their aggressiveness to fill processor queues. However, ADP and Q-OPT achieve better performance results in terms of the objective function since they complete tasks one by one without getting the infrastructure congested. Another reason is that Q-OPT and ADP allow for easier management of network load by delaying decisions in order to obtain better assignment options from vehicles that are arriving in the area or from vehicles that were not previously available. 3. Impact of Task Inter-Arrival Time on Temporal Results for Task Completion  
In Figure 8, the component-based average packet delays are presented in a stacked form. The labels on the top of the bars indicate the task inter-arrival time used for the given test group. Numerical values are also represented in Table 7. By observing the task life cycle, we can obtain more insights about the system operation and the potential improvements. The first thing to notice is that transmission delays account for the majority of system delays. Increasing available bandwidth while improving network congestion management and routing protocols could be highly helpful for the performance of such a network. The processing time has little effect with the current parameters. Only on AGG-1 is a 5-10% delay increase observed due to the queue wait procedure. The vehicles stay in the area for an average of 320 s with the current settings. By combining this data with the average system delay and task failure ratios, we can state that it is still possible to have a lower task inter-arrival time than 5 s, which would increase \(\overline{t}_{total}\) to over 300 s, without fully congesting the network. As a final analysis of this scenario, the system delay and task deadline of the tasks from simulations using the ADP algorithm are shown in a scatter graph in Figure 8(a) for Case 1.1, in Figure 8(b) for Case 1.2, and lastly in Figure 8(c) for Case 1.3. For each case, data from ten runs are reflected in the figures. It gives a good illustration of the strong effect of task generation rate on the system delay. In Case 1.1, high deviation around the average implies the system service instability at this level of load. Contrarily, in Case 1.3, our system runs at a steady state.