# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 5 Experiment Results
### Numerical Analysis  
For each dataset, we performed multiple executions to ensure the robustness and stability of the algorithm, facilitating subsequent comparison and analysis. Our data is based on simulated and synthetic data, reflecting remote sensing satellite tasks in the real world, providing insights into the performance of algorithms in real-world environments. Figure 3 provides the objective function values and optimization trends for 14 datasets, offering a detailed view of the performance of each algorithm  
In contrast, IDCE-GA and TS-SVMPP, although they perform reasonably in medium-sized scenarios, lack this dynamic adjustment capability. This limitation makes it difficult for them to maintain stable optimization when the task load increases. Similarly, MILP-STUDIO and RA-CNP rely on complex integer programming formulas, which can lead to a rapid increase in computational overhead in large-scale environments. Although VNE-AC sometimes produces competitive results in smaller networks, its static scheduling mechanism limits its adaptability to rapidly changing conditions in a wider and more complex environment. Though HEFT-LA and PA-LBIMM perform reasonably well, they still do not achieve the same level of optimization as HADRM. HEFT-LA lacks the dynamic resource mapping of HADRM, making it less effective in large-scale, dynamic environments. PA-LBIMM, while incorporating user-priority awareness, relies on static scheduling, limiting its adaptability in complex, fluctuating conditions. Table 5 shows the resource utilization percentages of each algorithm at different task scales, providing us with a detailed quantitative evaluation. For example, in dataset D14, the resource utilization rate of HADRM reached 77.5143%, higher than IDCE-GA's 53.2478% and TS-SVMPP's 62.8745%; in dataset D10, the resource utilization rate of HADRM reached 92.2541%, which is also better than the other comparative algorithms. These data fully demonstrate the ability of HADRM to more efficiently utilize available resources in large-scale complex task environments. As shown in Figure 4a, we first evaluate each algorithm's resource utilization in a static scenario by gradually increasing the task size. The results indicate that HADRM consistently maintains a high utilization rate, reflecting its advantage in real-time resource allocation as the task size grows. Next, to investigate how the iteration count affects performance, we conduct a parameter-tuning experiment. Specifically, we adjust the number of iterations and observe the algorithms' dynamic changes in resource usage. As illustrated in Figure 4b, HADRM continues to achieve the highest utilization among all methods over successive iterations, demonstrating robust and adaptive resource allocation under changing workload conditions. Its excellent performance is mainly attributed to its hierarchical adaptive decomposition and reinforcement learning-based resource mapping mechanism. Specifically, HADRM simplifies the scheduling process and reduces computational overhead by dynamically decomposing complex task sets into more manageable subproblems; meanwhile, its reinforcement learning module can adjust resource allocation decisions in real time based on constantly changing network conditions and task dependencies. However, although IDCE-GA and TS-SVMPP perform well in medium-scale scenarios, they lack the necessary dynamic adjustment capabilities, making it difficult for them to