Q‐learning‐based task offloading strategy for satellite edge computing

Summary In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three‐tier machine learning framework consisting of collaborative edge de...

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Bibliographic Details
Published in:International journal of communication systems Vol. 37; no. 5
Main Authors: Shuai, Jiaqi, Xie, Bo, Cui, Haixia, Wang, Jiahuan, Wen, Weichang
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 25.03.2024
Subjects:
Algorithms
Cloud computing
Computation offloading
Edge computing
Energy consumption
Machine learning
reinforcement learning
satellite edge computing
task offloading
ISSN:1074-5351, 1099-1131
Online Access:Get full text
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Summary:Summary In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three‐tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q‐learning‐based reinforcement learning offloading strategy in which both the time‐sensitive constraints and data requirements of the computation‐intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency. This paper studies the task offloading optimization problem in satellite edge computing environments. A three‐layer machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers is proposed. We propose a Q‐learning‐based reinforcement learning offloading strategy, which enables various types of tasks to select the most suitable satellite nodes for the computing deployment. This reduces the whole communication latency and energy consumption so as to enhance the offloading success rate.
Bibliography:Jiaqi Shuai and Bo Xie contribute equally.
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ISSN:1074-5351
1099-1131
DOI:10.1002/dac.5691