FAILED
首站-论文投稿智能助手
典型文献
SA-RSR:a read-optimal data recovery strategy for XOR-coded distributed storage systems
文献摘要:
To ensure the reliability and availability of data,redundancy strategies are always required for distributed storage systems.Erasure coding,one of the representative redundancy strategies,has the advantage of low storage overhead,which facilitates its employment in distributed storage systems.Among the various erasure coding schemes,XOR-based erasure codes are becoming popular due to their high computing speed.When a single-node failure occurs in such coding schemes,a process called data recovery takes place to retrieve the failed node's lost data from surviving nodes.However,data transmission during the data recovery process usually requires a considerable amount of time.Current research has focused mainly on reducing the amount of data needed for data recovery to reduce the time required for data transmission,but it has encountered problems such as significant complexity and local optima.In this paper,we propose a random search recovery algorithm,named SA-RSR,to speed up single-node failure recovery of XOR-based erasure codes.SA-RSR uses a simulated annealing technique to search for an optimal recovery solution that reads and transmits a minimum amount of data.In addition,this search process can be done in polynomial time.We evaluate SA-RSR with a variety of XOR-based erasure codes in simulations and in a real storage system,Ceph.Experimental results in Ceph show that SA-RSR reduces the amount of data required for recovery by up to 30.0%and improves the performance of data recovery by up to 20.36%compared to the conventional recovery method.
文献关键词:
作者姓名:
Xingjun ZHANG;Ningjing LIANG;Yunfei LIU;Changjiang ZHANG;Yang LI
作者机构:
School of Computer Science and Technology,Xi'an Jiaotong University,Xi'an 710049,China;Beijing Electronic Engineering General Research Institute,Beijing 100854,China
引用格式:
[1]Xingjun ZHANG;Ningjing LIANG;Yunfei LIU;Changjiang ZHANG;Yang LI-.SA-RSR:a read-optimal data recovery strategy for XOR-coded distributed storage systems)[J].信息与电子工程前沿(英文),2022(06):858-875
A类:
Erasure
B类:
SA,RSR,optimal,data,recovery,strategy,XOR,coded,distributed,storage,systems,To,ensure,reliability,availability,redundancy,strategies,always,required,coding,representative,has,advantage,low,overhead,which,facilitates,employment,Among,various,erasure,schemes,codes,becoming,popular,due,their,high,computing,speed,When,single,failure,occurs,such,process,called,takes,place,retrieve,failed,lost,from,surviving,nodes,However,transmission,during,usually,requires,considerable,amount,Current,research,focused,mainly,reducing,needed,encountered,problems,significant,complexity,local,In,this,paper,propose,random,algorithm,named,up,uses,simulated,annealing,technique,solution,that,reads,transmits,minimum,addition,done,polynomial,We,evaluate,variety,simulations,real,Ceph,Experimental,results,show,reduces,by,improves,performance,compared,conventional,method
AB值:
0.485026
相似文献
Harvesting random embedding for high-frequency change-point detection in temporal complex systems
Jia-Wen Hou;Huan-Fei Ma;Dake He;Jie Sun;Qing Nie;Wei Lin-Research Institute of Intelligent Complex Systems,Fudan University,Shanghai 200433,China;Centre for Computational Systems Biology,Institute of Science and Technology for Brain-Inspired Intelligence,Fudan University,Shanghai 200433,China;School of Mathematical Sciences,Soochow University,Suzhou 215006,China;Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine,Shanghai 200092,China;School of Mathematical Sciences and Shanghai Center for Mathematical Sciences,Fudan University,Shanghai 200433,China;Department of Mathematics,Department of Developmental and Cell Biology,and NSF-Simons Center for Multiscale Cell Fate Research,University of California,Irvine,CA 92697-3875,USA;Shanghai Key Laboratory for Contemporary Applied Mathematics,LNMS(Fudan University),and LCNBI(Fudan University),Shanghai 200433,China;State Key Laboratory of Medical Neurobiology,and MOE Frontiers Center for Brain Science,Institutes of Brain Science,Fudan University,Shanghai 200032,China
机标中图分类号,由域田数据科技根据网络公开资料自动分析生成,仅供学习研究参考。