Open Access

ARTICLE

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

by Yong Ma¹, Han Zhao², Kunyin Guo^3,*, Yunni Xia^3,*, Xu Wang⁴, Xianhua Niu⁵, Dongge Zhu⁶, Yumin Dong⁷

1 School of Computer Information Engineering, Jiangxi Normal University, Nanchang, 330000, China
2 School of Digital Industry, Jiangxi Normal University, Shangrao, 334000, China
3 The College of Computer Science, Chongqing University, Chongqing, 400044, China
4 College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, 400030, China
5 School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China
6 Electric Power Research Institute of State Grid Ningxia Electric Power Company Ltd., Yinchuan, 750002, China
7 College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China

* Corresponding Authors: Kunyin Guo. Email: ; Yunni Xia. Email:

(This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)

Computer Modeling in Engineering & Sciences 2024, 140(1), 907-927. https://doi.org/10.32604/cmes.2024.048759

Received 17 December 2023; Accepted 06 February 2024; Issue published 16 April 2024

Abstract

Mobile Edge Computing (MEC) is a technology designed for the on-demand provisioning of computing and storage services, strategically positioned close to users. In the MEC environment, frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery, ultimately enhancing the quality of the user experience. However, due to the typical placement of edge devices and nodes at the network’s periphery, these components may face various potential fault tolerance challenges, including network instability, device failures, and resource constraints. Considering the dynamic nature of MEC, making high-quality content caching decisions for real-time mobile applications, especially those sensitive to latency, by effectively utilizing mobility information, continues to be a significant challenge. In response to this challenge, this paper introduces FT-MAACC, a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance mechanisms. This approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached content. Furthermore, it relies on collaborative caching strategies based on multi-agent deep reinforcement learning models and establishes a fault-tolerance model to ensure the system’s reliability, availability, and persistence. Empirical results unequivocally demonstrate that FT-MAACC outperforms its peer methods in cache hit rates and transmission latency.

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Keywords

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