Distributed Gradient Descent Framework for Real-Time Task Offloading in Heterogeneous Satellite Networks

Yanbing Li, Yuchen Wu and Shangpeng Wang ()
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Yanbing Li: School of Cyber Science and Engineering, College of lnformation Science and Engineering, Xinjiang University, Urumqi 830046, China
Yuchen Wu: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Shangpeng Wang: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Mathematics, 2025, vol. 13, issue 4, 1-31

Abstract: Task offloading in satellite networks, which involves distributing computational tasks among heterogeneous satellite nodes, is crucial for optimizing resource utilization and minimizing system latency. However, existing approaches such as static offloading strategies and heuristic-based offloading methods neglect dynamic topologies and uncertain conditions that hinder adaptability to sudden changes. Furthermore, current collaborative computing strategies inadequately address satellite platform heterogeneity and often overlook resource fluctuations, resulting in inefficient resource sharing and inflexible task scheduling. To address these issues, we propose a dynamic gradient descent-based task offloading method. This method proposes a collaborative optimization framework based on dynamic programming. By constructing delay optimization and resource efficiency models and integrating dynamic programming with value iteration techniques, the framework achieves real-time updates of system states and decision variables. Then, a distributed gradient descent algorithm combined with Gradient Surgery techniques is employed to optimize task offloading decisions and resource allocation schemes, ensuring a precise balance between delay minimization and resource utilization maximization in dynamic network environments. Experimental results demonstrate that the proposed method enhances the global optimizing result by at least 1.97%, enhances resource utilization rates by at least 3.91%, and also reduces the solution time by at least 191.91% in large-scale networks.

Keywords: dynamic satellite networks; resource allocation; task offloading; collaborative optimization; gradient descent (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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