# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 5 Experiment Results
### Numerical Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & HEFT-LA [26] & PA-LBIMM [32] & IDCE-GA [30] & TS-SVMPP [31] & MILP-STUDIO [25] & RA-CNP [29] & VNE-AC [36] & HADRM \\ \hline \(D_{1}\) & 93.0214\% & 95.1215\% & 89.5412\% & 96.5874\% & 97.8541\% & 96.0415\% & 98.5415\% & **94.5214\%** \\ \(D_{2}\) & 92.6521\% & 94.4542\% & 86.9874\% & 97.5412\% & 96.9874\% & 97.1245\% & 98.1123\% & **94.1521\%** \\ \(D_{3}\) & 90.5426\% & 90.5124\% & 87.4578\% & 95.5874\% & 96.5874\% & 95.874\% & 97.5641\% & 97.5426\% \\ \(D_{4}\) & 86.0414\% & 87.5126\% & 74.5878\% & 87.2524\% & 88.5478\% & 86.4233\% & 88.6541\% & 87.7241\% \\ \(D_{5}\) & 87.0541\% & 88.5126\% & 72.5874\% & 86.0987\% & 87.4574\% & 86.0145\% & 88.3354\% & 88.6541\% \\ \(D_{6}\) & 84.0424\% & 84.9732\% & 75.2145\% & 82.5412\% & 89.5214\% & 81.5245\% & 84.5124\% & 85.1424\% \\ \(D_{7}\) & 86.0415\% & 88.7215\% & 76.5874\% & 83.0124\% & 91.0874\% & 82.8124\% & 84.5524\% & 88.2142\% \\ \(D_{8}\) & 81.5471\% & 83.7423\% & 72.8541\% & 79.8452\% & 88.2354\% & 80.2531\% & 80.5569\% & 83.5471\% \\ \(D_{9}\) & 82.5487\% & 83.6523\% & 69.8574\% & 76.4521\% & 85.0214\% & 76.8421\% & 77.3699\% & 83.5487\% \\ \(D_{10}\) & 89.5241\% & 92.4542\% & 68.5478\% & 73.1254\% & 83.2787\% & 72.6589\% & 73.5586\% & 92.2521\% \\ \(D_{11}\) & 81.0424\% & 82.0652\% & 64.5874\% & 71.8745\% & 81.0587\% & 71.0548\% & 73.5548\% & 81.5496\% \\ \(D_{12}\) & 81.0493\% & 82.0632\% & 61.2478\% & 70.1254\% & 70.0154\% & 69.3214\% & 72.2154\% & 81.5493\% \\ \(D_{13}\) & 79.0469\% & 87.7929\% & 57.5841\% & 66.5478\% & 76.5874\% & 65.8742\% & 67.5541\% & 80.2469\% \\ \(D_{14}\) & 76.5143\% & 77.9435\% & 53.2478\% & 62.8745\% & 72.5874\% & 61.8541\% & 63.2999\% & **77.5143\%** \\ \hline
**Average** & 85.0246\% & 86.6049\% & 72.2428\% & 79.3887\% & 90.1071\% & 79.3928\% & 88.0500\% & **83.1071\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Resource utilization across 14 datasets. reduces the dimensionality of problems by decomposing large-scale complex tasks into multiple easily manageable subtasks, and utilizes reinforcement learning modules to monitor network status and task dependency changes in real time, dynamically adjusting scheduling strategies and effectively shortening overall execution time. In contrast, IDCE-GA is based on genetic algorithms, and its evolutionary process converges slowly and is prone to becoming stuck in local optima, making it difficult to search for global optimal solutions in a timely manner when facing large-scale tasks. Although TS-SVMPP uses tabu search to improve local optimization, the search space rapidly expands when the number of tasks increases, resulting in a significant increase in computational burden. MILP-STUDIO relies on integer programming for solving, and its computational complexity increases exponentially, leading to a sharp increase in computation time in large-scale scenarios. RA-CNP adopts a static network performance model, and the scheduling strategy lacks responses to real-time changes in task load and network status. The fixed static scheduling mechanism of VNE-AC cannot effectively cope with the dynamic changes in task dependencies in complex environments, and its overall scheduling efficiency is greatly limited. For clarity of presentation, we conducted experiments on all 14 datasets but only show representative results for four of them in this table. The Table 7 provides a detailed display of two key metrics for task execution for each algorithm in four datasets (\(D_{1}\)-\(D_{4}\)). From the table, it can be seen that HADRM achieved the best performance in all datasets. For example, in dataset \(D_{1}\), HADRM's EQ reached 78 and its UQ was only 2, significantly better than those of IDCE-GA (EQ 75, UQ 5), TS-SVMPP (EQ 77, UQ 3), MILP-STUDIO (EQ 76, UQ 4), and VNE-AC (EQ 73, UQ 7). In dataset \(D_{2}\), HADRM led again with EQ 146 and UQ 14. In datasets \(D_{3}\) and \(D_{4}\), HADRM achieved EQs of 215 and 264, respectively, while its UQs were only 25 and 36.