Correlation-Aware Replica Prefetching Strategy to Decrease Access Latency in Edge Cloud  

作　　者：Yang Liang Zhigang Hu Xinyu Zhang Hui Xiao 

机构地区：[1]School of Computer Science and Engineering,Central South University,Changsha 410083,China [2]School of Informatics,Hunan University of Chinese Medicine,Changsha 410208,China [3]TCM Big Data Analysis Laboratory of Hunan,Hunan University of Chinese Medicine,Changsha 410208,China

出　　处：《China Communications》2021年第9期249-264,共16页中国通信（英文版）

基　　金：the National Natural Science Foundation of China(No.61602525,No.61572525);the Research Foundation of Education Bureau of Hunan Province of China(No.19C1391);the Natural Science Foundation of Hunan Province of China(No.2020JJ5775)。

摘　　要：With the number of connected devices increasing rapidly,the access latency issue increases drastically in the edge cloud environment.Massive low time-constrained and data-intensive mobile applications require efficient replication strategies to decrease retrieval time.However,the determination of replicas is not reasonable in many previous works,which incurs high response delay.To this end,a correlation-aware replica prefetching(CRP)strategy based on the file correlation principle is proposed,which can prefetch the files with high access probability.The key is to determine and obtain the implicit high-value files effectively,which has a significant impact on the performance of CRP.To achieve the goal of accelerating the acquisition of implicit highvalue files,an access rule management method based on consistent hashing is proposed,and then the storage and query mechanisms for access rules based on adjacency list storage structure are further presented.The theoretical analysis and simulation results corroborate that CRP shortens average response time over 4.8%,improves average hit ratio over 4.2%,reduces transmitting data amount over 8.3%,and maintains replication frequency at a reasonable level when compared to other schemes.

关 键 词：edge cloud access latency replica prefetching correlation-aware access rule 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]