# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 7 Performance Evaluation
### Experimental Results
#### 7.3.1 Impact of Task Inter-Arrival Time  
In Scenario 1, we applied three different task request inter-arrival times while maintaining the same expected number of vehicles. The APs deployed on the environment use 802.11 g mode, which has theoretical throughput of up to 54 Mbps but actually achieves much less due to overhead and path loss, so there was network congestion even at the _Case 1.3_, which has the highest task inter-arrival time. The maximum throughput in our simulation environment was 36 Mbps, which was achievable for a vehicle-to-UAV link under 150 m in length. Since many of the vehicles utilize the same interfaces, their share of the bandwidth becomes much less. * Impact of Task Inter-Arrival Time on Penalty Results  
Our first and main evaluation criterion is the performance analysis of the algorithms in terms of the main objective function \(\mathbb{D}\), which aims to minimize the weighted average of the prioritization-based penalty points. In Figure 7, the impact of varying task request intervals on \(\mathbb{D}\) for each algorithm can be seen. A cloud connection is more stable than a connection between two moving vehicles because the cloud side is stationary. Cloud offloading also has the advantage of faster processing and no queue waiting time. As a result, the Only-Cloud option performs better compared to other heuristics under light load. On the other hand, as request interval decreases, all algorithms exhibit an exponential increase in \(\mathbb{D}\). Among them, the Only-Cloud option is the most impacted one because the backhaul link to the cloud becomes a bottleneck, resulting in slower packet transfers, whereas our heuristics can distribute load to different processors. The main logic of AGG-1 for preserving download starting order for task processing is to wait for tasks that are older or chosen earlier due to their priority-deadline values. It does not seem to be making a meaningful difference in terms of \(\mathbb{D}\) compared to thenon-preserving version, AGG-2. Having oracle-type decisions and being theoretical output, Q-OPT seems to give superior results compared to the rest, especially as the load increases since it can make better decisions in advance while the rest make decisions with much less limited data. Among the online decision-makers, ADP outperforms the competition by a significant margin in all three cases. Along with \(\mathbb{D}\), we can see the service delivery skills of the algorithms in Table 6. For Case 1.1, in which the system is in its most loaded state, users face a high ratio of task offloading failures, which is due to task owners leaving the area before their task is successfully transmitted at a high percentage. In addition, an increase in the cloud ratio means the number of tasks in the controller pools has been increased. ADP has the highest cloud offloading ratio, which is expected since the processors do not download multiple tasks simultaneously, leading the pool to be more crowded. Still, ADP has the best service scores compared to AGG-1 and AGG-2, considering \(\mathbb{D}\) and task failure ratios. After analyzing the experimental data records, we have discovered two main reasons. Firstly, the task download ratio per processor is always less than 1, which is not the case for the other two heuristic algorithms. Thus, it leads to a less congested network and current offloading tasks' transmission times to be shortened. Secondly, since the task pool gets filled more easily, utilization of the cloud commences earlier, while AGG algorithms start to offload to the cloud after all their queues are full.