Open Access

ARTICLE

Offload Strategy for Edge Computing in Satellite Networks Based on Software Defined Network

by Zhiguo Liu^1,#, Yuqing Gui^1,#, Lin Wang^2,*, Yingru Jiang¹

1 Communication and Network Laboratory, Dalian University, Dalian, 116622, China
2 College of Environment and Chemical Engineering, Dalian University, Dalian, 116622, China

* Corresponding Author: Lin Wang. Email:
# These authors contributed equally to this work

(This article belongs to the Special Issue: Practical Application and Services in Fog/Edge Computing System)

Computers, Materials & Continua 2025, 82(1), 863-879. https://doi.org/10.32604/cmc.2024.057353

Received 15 August 2024; Accepted 21 October 2024; Issue published 03 January 2025

Abstract

Satellite edge computing has garnered significant attention from researchers; however, processing a large volume of tasks within multi-node satellite networks still poses considerable challenges. The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers, making it necessary to implement effective task offloading scheduling to enhance user experience. In this paper, we propose a priority-based task scheduling strategy based on a Software-Defined Network (SDN) framework for satellite-terrestrial integrated networks, which clarifies the execution order of tasks based on their priority. Subsequently, we apply a Dueling-Double Deep Q-Network (DDQN) algorithm enhanced with prioritized experience replay to derive a computation offloading strategy, improving the experience replay mechanism within the Dueling-DDQN framework. Next, we utilize the Deep Deterministic Policy Gradient (DDPG) algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks. Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches, effectively reducing task processing latency and thus improving user experience and system efficiency.

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Keywords

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