CRL： Efficient Concurrent Regeneration Codes with Local Reconstruction in Geo-Distributed Storage Systems  

作　　者：Quan-Qing Xu Wei-Ya Xi Khai Leong Yong Chao Jin 

机构地区：[1]Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore 138632, Singapore [2]Data Storage Institute, Agency for Science, Technology and Research, Singapore 138632, Singapore [3]Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632, Singapore

出　　处：《Journal of Computer Science & Technology》2018年第6期1140-1151,共12页计算机科学技术学报（英文版）

摘　　要：As a typical erasure coding choice, Reed-Solomon （RS） codes have such high repair cost that there is a penaltyfor high reliability and storage efficiency, thereby they are not suitable in geo-distributed storage systems. We present anovel family of concurrent regeneration codes with local reconstruction （CRL） in this paper. The CRL codes enjoy threebenefits. Firstly, they are able to minimize the network bandwidth for node repair. Secondly, they can reduce the numberof accessed nodes by calculating parities from a subset of data chunks and using an implied parity chunk. Thirdly, they arefaster than existing erasure codes for reconstruction in geo-distributed storage systems. In addition, we demonstrate howthe CRL codes overcome the limitations of the Reed-Solomon codes. We also illustrate analytically that they are excellent inthe trade-off between chunk locality and minimum distance. Furthermore, we present theoretical analysis including latencyanalysis and reliability analysis for the CRL codes. By using quantity comparisons, we prove that CRL（6, 2, 2） is only0.657x of Azure LRC（6, 2, 2）, where there are six data chunks, two global parities, and two local parities, and CRL（10,4, 2） is only 0.656x of HDFS-Xorbas（10, 4, 2）, where there are 10 data chunks, four local parities, and two global paritiesrespectively, in terms of data reconstruction times. Our experimental results show the performance of CRL by conductingperformance evaluations in both two kinds of environments： 1） it is at least 57.25% and 66.85% more than its competitorsin terms of encoding and decoding throughputs in memory, and 2） it has at least 1.46x and 1.21x higher encoding anddecoding throughputs than its competitors in JBOD （Just a Bunch Of Disks）. We also illustrate that CRL is 28.79% and30.19% more than LRC on encoding and decoding throughputs in a geo-distributed environment.

关 键 词：CONCURRENT REGENERATION CODE local reconstruction geo-distributed storage system 

分 类 号：TP[自动化与计算机技术]