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

作     者：Yong Ma Han Zhao Kunyin Guo Yunni Xia Xu Wang Xianhua Niu Dongge Zhu Yumin Dong 

作者机构：School of Computer Information EngineeringJiangxi Normal UniversityNanchang330000China School of Digital IndustryJiangxi Normal UniversityShangrao334000China The College of Computer ScienceChongqing UniversityChongqing400044China College of Mechanical and Vehicle EngineeringChongqing UniversityChongqing400030China School of Computer and Software EngineeringXihua UniversityChengdu610039China Electric Power Research Institute of State Grid Ningxia Electric Power Company Ltd.Yinchuan750002China College of Computer and Information ScienceChongqing Normal UniversityChongqing401331China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第140卷第7期

页      面：907-927页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0701[理学-数学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Innovation Fund Project of Jiangxi Normal University(YJS2022065) the Domestic Visiting Program of Jiangxi Normal University 

主　　题：Mobile edge networks mobility fault tolerance cooperative caching multi-agent deep reinforcement learning content prediction 

摘      要：Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to *** 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 ***,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 *** the dynamic nature ofMEC,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 *** 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 *** approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached ***,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and *** results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.