Year: 2025
Type: article
Abstract: In this paper, we investigate the Adaptive Service Migration (ASM) problem in dynamic satellite edge computing networks, focusing on Low Earth Orbit satellites with time-varying inter-satellite links. We formulate the ASM problem as a constrained optimization problem, aiming to minimize overall service cost, which includes both interruption cost and processing cost. To address this problem, we propose ASM-DRL, a deep reinforcement learning approach based on the soft Actor-Critic framework. ASM-DRL introduces an adaptive entropy adjustment mechanism to dynamically balance exploration and exploitation, and adopts a dual-Critic architecture with soft target updates to enhance training stability and reduce Q-value overestimation. Extensive simulations show that ASM-DRL significantly outperforms baseline approaches in reducing service cost.
Source: Electronics
Institution: Nanjing University of Posts and Telecommunications
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415909086