## 4 Methodology
### Domain Clustering Based on Multi-Path Transmission
#### 4.2.6 Problem Modeling and Optimization Algorithm Design

Table 5: Task execution time(in seconds) across four datasets. bust data acquisition mechanisms, which might introduce additional overhead in practical deployments. Future work should focus on further optimizing the algorithm's scalability and enhancing its adaptability to diverse and unpredictable network conditions, to fully realize its potential in satellite network resource scheduling and task allocation. Overall, the experimental analysis validated the efficacy and feasibility of CMPT in optimizing resource allocation and task scheduling in satellite networks, showcasing its aptitude for comprehensively enhancing system performance and management efficiency. ## 6 Conclusions

This paper addressed the issues of low resource utilization and high system latency in dynamic satellite networks concerning resource allocation and task scheduling. We proposed a collaborative optimization framework based on game theory and multi-path transmission--CMPT. This method integrates a cooperative game model, Random Linear Network Coding (RLNC), Software-Defined Networking (SDN)-based domain clustering strategies, and the Lagrangian multiplier method, aiming to achieve fairness and efficiency in resource allocation. Despite a latency slightly higher than some comparative algorithms, its performance remains outstanding in large-scale and high-dimensional problems. Future research will focus on further optimizing the CMPT algorithm to significantly reduce task latency for small-scale datasets, extending the resource allocation. The experimental results demonstrate that the CMPT algorithm reduced the resource allocation task execution time by at least 18.03% compared to traditional methods and improved task scheduling efficiency by at least 14.01%. Although on certain small-scale datasets, the task latency of CMPT was slightly higher than some comparative algorithms, future research will focus on adapting it to more complex and heterogeneous satellite network environments--including multiple types of satellite nodes and diversified task requirements--and explore the algorithm's scalability and performance optimization for larger satellite constellations. Conceptualization, S.W. ; methodology, S.W. and Y.W. ; software, C.Z. ; validation, Y.W. and C.Z. ; formal analysis, S.W. ; investigation, Y.W. ; resources, S.W. and J.L. ; data curation, Y.W. ; writing original draft preparation, S.W. ; writing review and editing, S.W. and C.Z. ; visualization, Y.W. and C.Z. ; supervision, L.L. and J.L. ; project administration, J.L. ; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (Grant No. U2003208 and No. 61972398), the Science and Technology Plan of Hunan Province (Grant No. 2016 TP1003), the Key Technology R&D Program of Hunan Province (Grant No. 2018 GK2052), the Foundation for Equipment Procurement (Grant No. 6141A020227), and the Theory and Method of Multi-Satellite Collaborative Application Foundation (Grant No. 2019-JCJQ-ZD-342-00). No new data were created or analyzed in this study. Data sharing is not applicable to this article. The authors declare no conflicts of interest. ## References

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