Dynamic Computing Migration in Space-Ground Integrated Networks

The proliferation of edge computing has significantly reduced latency and improved network responsiveness, but its full potential remains unrealized due to challenges in managing the dynamic migration of mobile station (MSs). Existing research predominantly focuses on terrestrial cellular networks, overlooking the complexities of space-ground integrated systems. This paper addresses this gap by introducing a combined network architecture that enables seamless MS migration. We propose a distributed migration strategy assisted by the core network, leveraging a Distributed-Deployed Deep Q-Network (DP-DQN) algorithm. The proposed framework minimizes the computation delay and system overhead while optimizing resource allocation. Simulation results demonstrate that our approach outperforms traditional migration methods, achieving lower average delay and reduced network overhead.

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Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/ccpqt64497.2024.00037
Primary Topic
Opportunistic and Delay-Tolerant Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Dynamic Computing Migration in Space-Ground Integrated Networks

Juzhen Wang, Qing Xue, Mingliang Deng, Zhidu Li et al.
Opportunistic and Delay-Tolerant Networks
article

Dynamic Computing Migration in Space-Ground Integrated Networks

Juzhen Wang, Qing Xue, Mingliang Deng, Zhidu Li, Hongbo Zhang, Pengxiang Li
article en

Abstract

The proliferation of edge computing has significantly reduced latency and improved network responsiveness, but its full potential remains unrealized due to challenges in managing the dynamic migration of mobile station (MSs). Existing research predominantly focuses on terrestrial cellular networks, overlooking the complexities of space-ground integrated systems. This paper addresses this gap by introducing a combined network architecture that enables seamless MS migration. We propose a distributed migration strategy assisted by the core network, leveraging a Distributed-Deployed Deep Q-Network (DP-DQN) algorithm. The proposed framework minimizes the computation delay and system overhead while optimizing resource allocation. Simulation results demonstrate that our approach outperforms traditional migration methods, achieving lower average delay and reduced network overhead.

Chongqing University of Posts and Telecommunications (CN), China Telecom (China) (CN), China Telecom (CN), Hangzhou Dianzi University (CN)
Natural Science Foundation of Chongqing, Chongqing Municipal Education Commission
Reduced inequalities
Openalex Percentile: Top 67%
Opportunistic and Delay-Tolerant Networks
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