A comprehensive survey on computation offloading using reinforcement learning

Junyi Liang; Xuehao Qi; Jinmei Yang; Fang Zhang

doi:10.1117/12.3011461

7 December 2023 A comprehensive survey on computation offloading using reinforcement learning

Junyi Liang, Xuehao Qi, Jinmei Yang, Fang Zhang

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129411O (2023) https://doi.org/10.1117/12.3011461
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The continuous development of computing and communication technologies, as well as the emergence of new applications such as autonomous driving, augmented reality, and industrial Internet of Things, have posed significant challenges to the computing and storage capabilities of terminal devices. We need a new computing paradigm to provide high-speed and low-latency computing services to meet the needs of these new applications. While cloud computing technology offers abundant computing power, it often fails to meet the latency requirements of these applications due to the long distance between cloud servers and terminals. To address this issue, the network paradigm of edge computing has been introduced. One of the key problems in edge computing applications is how to offload tasks generated by terminals effectively. Previous research has shown that reinforcement learning methods are effective approaches to tackle computation offloading. In this article, we provide a comprehensive survey and summary of the application of reinforcement learning/deep reinforcement learning in the context of task offloading and analyze possible future directions.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junyi Liang, Xuehao Qi, Jinmei Yang, and Fang Zhang "A comprehensive survey on computation offloading using reinforcement learning", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129411O (7 December 2023); https://doi.org/10.1117/12.3011461

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