A study of reinforcement learning for offloading of edge computing tasks

Yuhan Liu; Zhibin Liu; Zhenyou Zhou

doi:10.1117/12.2673680

22 May 2023 A study of reinforcement learning for offloading of edge computing tasks

Yuhan Liu, Zhibin Liu, Zhenyou Zhou

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126400V (2023) https://doi.org/10.1117/12.2673680
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

With the development of wireless network technology, it becomes possible to transfer huge amount of data from the center to the edge server computing. When deciding what kind of data tasks to offload to the edge server, we need to make a prudent decision. Traditional mathematical computing methods are computationally intensive, require computation all the time, and have high processor hardware requirements. We use the Actor-Critic algorithm to model the edge computing environment as a binary offloading process. Applying machine learning algorithms to the edge computing environment allows the intelligence of machines to replace a large number of mathematical operations. The Actor-Critic algorithm is highly adaptive and can effectively solve the current problems faced by edge computing, such as large computational volume and narrow application surface.

Citation Download Citation

Yuhan Liu, Zhibin Liu, and Zhenyou Zhou "A study of reinforcement learning for offloading of edge computing tasks", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126400V (22 May 2023); https://doi.org/10.1117/12.2673680

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