# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 4 Simulation Experiment
### Multi-DAG Task Load Balancing Experiment  
Tables 2-5, respectively, represent the number of tasks assigned by of the multiple edge computing nodes (\(m=3\), 5, 7, 15) during random multi-DAG generation. To assess the load balancing performance of the four algorithms, a performance analysis was conducted, and we defined the load balancing factor as  
\[\sigma=\sqrt{\frac{\sum_{i=1}^{m}\mid avgload-load_{i}\mid}{m}} \tag{11}\]  
where \(avgload\) represents the average load of the batch of nodes; \(load_{i}\) represents the load of node \(i\); and the smaller the factor, the better the load performance. The load balancing factor for the Fairness algorithm, Composite priority algorithm, Cache-Composite priority algorithm, and LB-HEFT algorithm can be obtained from Tables 2-5, as shown in Figure 8.  
Figure 7: Unfairness degree comparison diagram.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Fairness** & **Composite Priority** & **Cache-Composite Priority** & **LB-HEFT** \\ \hline Node 1 & 21 & 16 & 18 & 17 \\ Node 2 & 17 & 18 & 17 & 16 \\ Node 3 & 21 & 19 & 19 & 16 \\ Node 4 & 15 & 17 & 17 & 17 \\ Node 5 & 17 & 19 & 17 & 17 \\ Node 6 & 12 & 15 & 16 & 18 \\ Node 7 & 15 & 14 & 14 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of node tasks (edge computing node \(m=7\)).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Fairness** & **Composite Priority** & **Cache-Composite Priority** & **LB-HEFT** \\ \hline Node 1 & 42 & 35 & 45 & 40 \\ Node 2 & 44 & 47 & 39 & 39 \\ Node 3 & 32 & 36 & 34 & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of node tasks (edge computing node \(m=3\)).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Fairness** & **Composite Priority** & **Cache-Composite Priority** & **LB-HEFT** \\ \hline Node 1 & 8 & 8 & 8 \\ Node 2 & 9 & 9 & 8 & 8 \\ Node 3 & 8 & 8 & 8 & 8 \\ Node 4 & 7 & 8 & 8 & 8 \\ Node 5 & 7 & 9 & 9 & 8 \\ Node 6 & 9 & 8 & 8 & 8 \\ Node 7 & 7 & 8 & 8 & 8 \\ Node 8 & 8 & 7 & 7 & 7 \\ Node 9 & 9 & 7 & 7 & 7 \\ Node 10 & 7 & 8 & 8 & 8 \\ Node 11 & 9 & 7 & 8 & 8 \\ Node 12 & 8 & 8 & 7 & 8 \\ Node 13 & 7 & 8 & 8 & 8 \\ Node 14 & 8 & 7 & 8 & 8 \\ Node 15 & 7 & 8 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of node tasks (edge computing node \(m=15\)).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Fairness** & **Composite Priority** & **Cache-Composite Priority** & **LB-HEFT** \\ \hline Node 1 & 42 & 35 & 45 & 40 \\ Node 2 & 44 & 47 & 39 & 39 \\ Node 3 & 32 & 36 & 34 & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of node tasks (edge computing node \(m=5\)).  
Tables 6-9 show the task completion degree comparisons of the Fairness algorithm, Composite priority algorithm, Cache-Composite priority algorithm, and LB-HEFT algorithm executing multiple random DAGs (\(n=\) 5, 10, 15) on edge computing nodes (\(m=\) 3, 5, 7, 15). In Table 6 (\(m=\) 3), the differences are pronounced. The task completion degree of the Fairness algorithm was lower than that of the Composite priority algorithm, LB-HEFT algorithm, and Cache-Composite priority algorithm. All four algorithms exhibited a declining trend in task completion degree, with the Fairness algorithm showing a significant fluctuation as task numbers increased. With the increase in edge computing nodes, the task completion degree of the Cache-Composite priority algorithm consistently exceeded 90%. The reason for this is that the Fairness algorithm did not consider the tolerance latency of the task, and the Cache-Composite priority algorithm added this factor. Consequently, during task scheduling, the priority of DAG tasks increased with time, which influenced the results.