Multiresource allocation in dynamic MEC systems: a continual learning-based approach

Shutao Zhan; Jin Mao; Zhipeng Chu; Ke Xiong; Pingyi Fan; Khaled B. Letaief

doi:10.1117/12.3031365

19 July 2024 Multiresource allocation in dynamic MEC systems: a continual learning-based approach

Shutao Zhan, Jin Mao, Zhipeng Chu, Ke Xiong, Pingyi Fan, Khaled B. Letaief

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131817Q (2024) https://doi.org/10.1117/12.3031365
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Recently, mobile edge computing (MEC) has been considered as a promising solution to address the challenges arising from the growing computationally intensive applications and limited computation capability of mobile devices. Thus, the resource allocation is crucial for ensuring the system performance of MEC. Recent deep learning-based resource allocation (DLRA) methods achieve preferable performance in real-time. However, in practical scenarios, the number of users often changes, which leads to a decrease in the performance of DLRA methods. Thus, once the number of users changes, the traditional DLRA methods need to retrain their neural networks, which consumes a lot of computing resources. To address this issue, this paper proposes a continual learning-based resource allocation (CLRA) method to jointly optimize multiple resource including offloading decision, power control, time slot assignment and computation resource distribution in MEC system. Our CLRA method is able to avoid forgetting previous knowledge when the number of users in the MEC system changes by selecting a small number of the most representative samples from the past and relearning. Experimental results show that, our proposed CLRA method achieves a better performance than the traditional DLRA method in terms of the higher quality of service's (QoS's) satisfaction rate and the less energy consumption.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shutao Zhan, Jin Mao, Zhipeng Chu, Ke Xiong, Pingyi Fan, and Khaled B. Letaief "Multiresource allocation in dynamic MEC systems: a continual learning-based approach", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131817Q (19 July 2024); https://doi.org/10.1117/12.3031365

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KEYWORDS

Education and training

Neural networks

Data modeling

Computing systems

Mathematical optimization

Mobile devices

Dynamical systems

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