# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 5 Experiment Results
### Numerical Analysis
These data fully demonstrate the advantages of HADRM in terms of task execution quality and stability. The trend of task execution rate in Figure 6 further illustrates the dynamic changes in the performance of various algorithms at different task scales. From the graph, it can be seen that as the task size increases, and HADRM consistently maintains high execution quality and low uncertainty, demonstrating its ability to effectively schedule tasks and reduce errors in high-load environments. This outstanding performance is mainly attributed to the advanced task decomposition and real-time scheduling mechanism adopted by HADRM, which can dynamically split complex tasks into manageable subtasks and flexibly adjust  
Figure 5: Comparative task execution times in static and iterative scenarios. (**a**) Task execution times. (**b**) Task execution times in different iterations. scheduling strategies based on real-time feedback, thereby ensuring efficient and stable execution in large-scale task environments. In contrast, IDCE-GA relies on traditional genetic algorithms, while TS-SVMPP introduces tabu search to improve local optimization, but the search space rapidly expands when the task size increases. The integer programming method used by MILP-STUDIO has high computational complexity, while RA-CNP is limited by static network models, and the fixed scheduling mechanisms of VNE-AC cannot easily balance efficiency and low uncertainty in large-scale complex environments. HEFT-LA and PA-LBIMM demonstrate competitive performance in terms of makespan and resource utilization but still fall behind HADRM. While they offer improvements in load balancing and user-priority handling, their task execution time and resource utilization are less optimized compared to HADRM's more efficient scheduling. This is mainly due to the lack of dynamic task mapping in HEFT-LA and the trade-off between VIP and ordinary tasks in PA-LBIMM. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithms** & \(\mathbf{D_{1}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{D_{3}}\) & \(\mathbf{D_{4}}\) & \(\mathbf{D_{5}}\) & \(\mathbf{D_{6}}\) & \(\mathbf{D_{7}}\) \\ \hline HEFT-LA [26] & 320.35 & 810.21 & 1070.35 & 2600.22 & 1800.23 & 4350.25 & 8650.65 \\ PA-LBIMM [32] & 325.46 & 815.21 & 1045.22 & 2558.75 & 1760.35 & 4480.77 & 8664.25 \\ IDCE-GA [30] & 354.21 & 412.48 & 854.21 & 2854.33 & 3145.48 & 4854.33 & 9124.21 \\ TS-SVMPP [31] & 220.13 & 390.21 & 705.21 & 2175.13 & 2385.66 & 4550.48 & 4789.15 \\ MILP-STUDIO [25] & 198.13 & 356.48 & 685.21 & 1954.33 & 2187.48 & 3845.33 & 4201.21 \\ RA-CNP [29] & 245.66 & 410.24 & 710.13 & 2204.13 & 2405.66 & 4900.54 & 5250.33 \\ VNE-AC [36] & 215.66 & 452.16 & 769.35 & 2335.27 & 2546.29 & 4685.89 & 4961.23 \\
**HADRM** & **314.25** & **798.21** & **1044.35** & **2554.22** & **1752.23** & **4265.25** & **8547.65** \\ \hline
**Algorithms** & \(\mathbf{D_{8}}\) & \(\mathbf{D_{9}}\) & \(\mathbf{D_{10}}\) & \(\mathbf{D_{11}}\) & \(\mathbf{D_{12}}\) & \(\mathbf{D_{13}}\) & \(\mathbf{D_{14}}\) \\ \hline HEFT-LA [26] & 5700.65 & 7630.33 & 9764.33 & 12,850.33 & 10,250.66 & 11,300.65 & 29,950.15 \\ PA-LBIMM [32] & 5634.82 & 7637.54 & 9684.33 & 12,647.54 & 10,124.76 & 11,117.65 & 30,052.54 \\ IDCE-GA [30] & 6541.15 & 12,548.48 & 15,847.33 & 18,457.13 & 21,874.48 & 27,541.21 & 39,874.13 \\ TS-SVMPP [31] & 5987.45 & 12,012.48 & 14,985.21 & 19,458.33 & 22,514.48 & 26,847.45 & 39,874.13 \\ MILP-STUDIO [25] & 5001.15 & 10,845.48 & 13,487.33 & 17,845.13 & 20,541.48 & 24,847.21 & 37,254.13 \\ RA-CNP [29] & 6100.15 & 12,245.66 & 15,234.66 & 19,852.13 & 22,841.66 & 27,041.66 & 40,532.13 \\ VNE-AC [36] & 6553.49 & 12,509.37 & 15,660.33 & 20,053.44 & 23,360.23 & 27,654.12 & 41,025.67 \\
**HADRM** & **5562.65** & **7536.33** & **9564.33** & **12,541.33** & **10,032.66** & **11,054.65** & **29,652.15** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Task execution time across 14 datasets. Figure 6: Task execution rates of eight algorithms across four datasets.