## 5 Experiment Results
### Experimental Conclusion  
In this section, we constructed experimental scenarios on our self-developed Common Satellite Tool Kit (CSTK) platform to simulate the dynamic changes of satellite nodes, ground stations, user terminals, and inter-satellite communication links. This was performed to validate the effectiveness of the proposed task scheduling and resource optimization model in minimizing system latency and maximizing resource utilization. The experimental results demonstrate that the D-GTOM algorithm exhibits outstanding performance across satellite networks of varying scales, significantly outperforming five traditional comparative optimization algorithms (Differential Evolution, Genetic Algorithm, Particle Swarm Optimization, ABIO, and Simulated Annealing). Specifically, the D-GTOM algorithm achieved notable improvements in objective function values, resource utilization rates, and algorithm running times, particularly showcasing greater scalability and computational efficiency in large-scale and high-dimensional problems. These achievements are attributed to the collaborative optimization framework based on dynamic programming employed by the D-GTOM algorithm, as well as the effective integration of a multi-objective optimization model with Gradient Surgery techniques. This combination enables fair and efficient resource allocation in complex and dynamic network environments. Overall, the experiments validate the superiority and practicality of the D-GTOM algorithm in satellite network resource scheduling and task offloading, providing robust support for the efficient management of future large-scale satellite networks.  
In satellite networks, implementing D-GTOM requires the careful consideration of communication and integration with existing infrastructure. The algorithm relies on real-time communication between satellites and ground stations to share resource information and offloading decisions. This communication overhead grows with the number of satellites and the frequency of updates. To manage this, lightweight protocols like gossip or low-latency message-passing can be adopted to reduce global synchronization delays and ensure scalability. As for integration with existing satellite network infrastructure, particularly in heterogeneous LEO constellations, D-GTOM must adapt to the varying computational, storage, and communication capabilities of each satellite. This requires compatibility with satellite communication protocols, considering dynamic orbital movements, varying link capacities, and latency fluctuations. Additionally, integration with existing network management systems must ensure that task offloading decisions are harmonized with resource allocation models, enabling smooth scalability without disrupting legacy systems. By addressing these communication and integration challenges, D-GTOM can effectively be deployed within existing satellite network frameworks.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{5}{c}{**Task Quantity**} \\ \cline{2-6}  & **600** & **900** & **1500** & **2400** & **3600** \\ \hline TSA [42] & 2577.0 & 3751.0 & 5650.0 & 9293.0 & 6667.0 \\ AGA-ES [41] & 2354.0 & 3459.0 & 5453.0 & 7663.0 & 5727.25 \\ QPSO [39] & 1767.0 & 2557.0 & 5254.0 & 6803.0 & 4595.25 \\ DE [40] & 1954.0 & 2851.0 & 5893.0 & 8420.0 & 6029.5 \\ ABIO [38] & 2112.0 & 3156.0 & 4886.0 & 9210.0 & 6085.0 \\
**D-GTOM** & **1534.0** & **2253.0** & **4740.0** & **6185.0** & **4933.0** \\ \hline \hline \end{tabular} Note. This table presents the execution time (in seconds) of different scheduling algorithms in a large-scale satellite dataset as the task quantity increases. The execution time reflects the computational cost required to generate a feasible scheduling solution. The results for D-GTOM are highlighted in bold because it is the proposed method in this study.  
\end{table}
Table 7: Scheduling scheme execution time in large-scale satellite dataset with growing task quantity.