ACPR: Adaptive Classification Predictive Repair Method for Different Fault Scenarios

doi:10.1109/ACCESS.2023.3346881

	ACPR: Adaptive Classification Predictive Repair Method for Different Fault Scenarios
	Song, Ying 1,2,3; Zheng, Peisen 1,2; Tian, Yingai 1; Wang, Bo 4
	2024
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	12 页码:4631-4641
摘要	Erasure codes are widely used in large-scale distributed storage systems due to their high efficiency and reliability, but they also face extremely high repair penalties when data corruption occurs. At present, machine learning methods can accurately predict the next failure time and type of machine nodes. Based on this, in order to solve the problem of unnecessary repair traffic caused by temporary failures, as well as the more degraded reads of high-frequency accessed data due to longer failure time of such data in existing repair methods, we propose an Adaptive Classification Predictive Repair method (ACPR) for different fault scenarios. By categorizing the failed blocks into high-risk and low-risk based on the failure type of the soon-to-fail (STF) node and the access heat of STF blocks, ACPR can perform adaptive predictive repair. By quickly repair high-risk blocks to ensure data availability while delaying the repair of low-risk blocks, a large amount of unnecessary repair traffic caused by temporary node failures in the cluster is avoided. Alibaba Cloud Elastic Compute Service (ECS) experiments results show that compared with FastPR and ECPipe, ACPR can shorten the repair time per data block by up to 15.2% and 33.5%, respectively. Moreover, ACPR can reduce repair traffic by up to 74.1% and 84.4%, respectively.
关键词	Distributed storage system data recovery erasure coding
DOI	10.1109/ACCESS.2023.3346881
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001142755400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38432
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Song, Ying
作者单位	1.Beijing Informat Sci & Technol Univ, Beijing 100101, Peoples R China 2.Beijing Informat Sci & Technol Univ, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100086, Peoples R China 4.Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou 450002, Peoples R China
推荐引用方式 GB/T 7714	Song, Ying,Zheng, Peisen,Tian, Yingai,et al. ACPR: Adaptive Classification Predictive Repair Method for Different Fault Scenarios[J]. IEEE ACCESS,2024,12:4631-4641.
APA	Song, Ying,Zheng, Peisen,Tian, Yingai,&Wang, Bo.(2024).ACPR: Adaptive Classification Predictive Repair Method for Different Fault Scenarios.IEEE ACCESS,12,4631-4641.
MLA	Song, Ying,et al."ACPR: Adaptive Classification Predictive Repair Method for Different Fault Scenarios".IEEE ACCESS 12(2024):4631-4641.