# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 4 Simulation Experiment
### Multi-DAG Task Completion Time and Energy Consumption Experiment  
To validate the performance of the proposed multi-DAG task scheduling algorithm, in the simulation experiments, we randomly generated various edge computing nodes (\(m=3\), 5, 7, 15) and multiple random DAGs (\(n=5\), 10, 15) in the satellite network for testing and conducted 50 experiments on the Composite priority algorithm, Fairness algorithm, LB-HEFT algorithm, and Cache-Composite priority algorithm, respectively. The comparison diagram of average completion time and total energy consumption is shown in Figures 5 and 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Edge computing node processing speed \(h_{j}\) & 200\(\sim\)300 MIPS \\ Subtask workload \(L_{i}\) & 20\(\sim\)100 MI \\ Edge computing node power \(P_{j}^{work}\) & 300\(\sim\)350 W \\ Inter-satellite transmission power \(P_{pass}\) & 1\(\sim\)4 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: System simulation parameters.  
Figure 4: Iridium constellation diagram.  
The comparison diagram of DAG completion time is shown in Figure 5, where the completion time of the Composite priority algorithm and LB-HEFT algorithm is superior to that of the Fairness algorithm, showing an improvement of nearly 5% in performance. As the number of tasks increased, the gap gradually decreased. The reason for this phenomenon is that the Composite priority algorithm needs to consider the priority of each DAG task. When the number of tasks increased with a constant number of edge computing nodes, the weight of the DAG task priority increased, thus affecting the results. The introduction of the Cache-Composite priority algorithm significantly improved the performance, with the completion time performance increasing by nearly 20%. The total energy consumption comparison diagram is shown in Figure 6. Due to the Composite priority algorithm considering not only completion time but also energy consumption factors, its energy consumption was superior to that of the Fairness algorithm, resulting in a reduction in the energy consumption of nearly 7%. Similarly, the performance of the Composite priority algorithm with caching was enhanced by almost 25% compared to the Fairness algorithm. Because the LB-HEFT algorithm does not optimize for energy consumption, its energy consumption performance was comparable to that of the Fairness algorithm.  
In summary, the performance of the Composite priority algorithm and LB-HEFT algorithm showed a slight improvement compared to that of the Fairness algorithm in terms of both DAG completion time and energy consumption. Building upon the Composite priority algorithm, the introduction of a satellite SDN controller public task management module employing the Cache-Composite priority algorithm, which identifies and caches public subtasks, resulted in a significant performance improvement compared to the Fairness algorithm.  
Figure 5: Average completion time comparison diagram.  
Figure 6: Total energy consumption comparison diagram.