Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks

TL;DR

This paper introduces a deep reinforcement learning algorithm for task offloading in satellite-terrestrial edge networks, reducing delay and runtime while maintaining near-optimal performance in remote regions with fast fading channels.

Contribution

It proposes the DRTO algorithm that accelerates learning and adapts offloading decisions based on current channel states, addressing limitations of existing methods.

Findings

DRTO achieves near-optimal offloading cost.

DRTO significantly reduces runtime compared to existing algorithms.

The approach is effective in fast fading satellite-terrestrial channels.

Abstract

In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resource-constrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high. In this paper, we study the task offloading problem in satellite-terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.