# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
## 3 System Models and Problem Formulation
### Task Scheduling Model
#### 3.3.1 Scheduling Model Based on Subtask Degree
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline The maximum available computing resources for satellite MEC & 10 Gcycles/s \\ Local computing resource & 2.5 Gcycles/s \\ Satellite MEC available bandwidth resources & 10 MHz \\ Subtask size & [50 k/b, 100 kb] \\ Number of subtasks per DAG & 10–15 \\ CPU computing power & 1000 cycles/bit \\ The application tolerates latency & 0.5–1.5 s \\ Total number of iterations & 200 \\ Sparrow population & 100 \\ Proportion of producers & 10\% \\ Proportion of investigators & 20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. rate of the method proposed in this paper is higher than the AS, indicating that the satellite ground processing together is effective, and also illustrates the importance of proper task offloading decisions. The completion rate of the average allocation algorithm (AV) was 90.9% [33]. The AV has a lower completion rate than the method proposed in this paper, indicating the importance of resource allocation for tasks offloaded to the satellite. As shown in Figure 4, with the increase in satellite communication resources, the task completion rates of all schemes show an upward trend. In addition, the AS and RA have a lower task completion rate than the other two algorithms, while the TIDISSA has the highest completion rate. When the satellite communications resources were 14 MHz, the completion rate of TIDISSA was 90.9%, while that of RA and AS were 63.63% and 54.54%, respectively. The more communication resources a satellite has, the more communication resources can be allocated to a task, which reduces task computation latency. However, the communication resources of the satellite's MEC server are still limited and, currently, there is only one satellite node, which can process limited tasks. So, offload a part of the application to the satellite for computation. In AS, each application offloads its tasks to the satellite for calculation, so this leads to its lower task completion rate, and many applications cannot successfully process. However, since the random decision is not optimal, its completion rate is also lower. The completion rate of the method proposed in this paper is higher than that of AS, indicating that the satellite ground processing together is effective, and also illustrates the importance of appropriate task offloading decisions. The completion rate of the AV is 81.81%, which is lower than the completion rate of the method proposed in this paper, indicating the importance of resource allocation for the tasks offloaded to the satellite. As shown in Figure 5, as the number of DAGs increases, the task completion rates of all schemes show a downward trend. In addition, the AS and RA have a low task completion rate, while the TIDISSA has a consistently higher completion rate. When the number of DAGs to be processed is 16, the completion rate of TIDISSA was 12.5%, while that of RA and AS were 6.25% and 0%, respectively. Because the computing resources of the satellite's MEC server are still limited and there is currently only one satellite node, the tasks that can be processed are limited. When the number of DAGs continues to increase, the processing success rate will decrease. In the AS, each application offloads its tasks to the satellite for calculation. As the number of DAGs increases, many applications cannot be successfully processed. However, since the random decision is not optimal, its completion rate is also lower. The completion rate of the method proposed in this paper is consistently higher than the AS, indicating that the satellite ground co-processing is effective, and also indicates

Figure 3: DAG completion rate versus satellite computing resources. the importance of the task offloading decision explained. The completion rate of the AV is 6.25%, which is lower than the completion rate of the method proposed in this paper, indicating the importance of resource allocation for the tasks offloaded to the satellite.