首页> 外文会议>IEEE International Congress on Big Data >STORE: Data recovery with approximate minimum network bandwidth and disk I/O in distributed storage systems
【24h】

STORE: Data recovery with approximate minimum network bandwidth and disk I/O in distributed storage systems

机译:存储:分布式存储系统中具有大约最小网络带宽和磁盘I / O的数据恢复

获取原文

摘要

Recently, traditional erasure codes such as Reed-Solomon (RS) codes have been increasingly deployed in many distributed storage systems to reduce the large storage overhead incurred by the widely adopted replication scheme. However, these codes require significantly high resources with respect to network bandwidth and disk I/O during recovery of missing or unavailable data. It is referred as the recovery problem. In this paper, we dedicate to integrating exact minimum bandwidth regenerating codes into practical systems to solve the recovery problem. We design an implementation friendly storage code with the recently proposed BASIC framework and ZigZag decodable code for saving recovery bandwidth and disk I/O. We build a system called STORE based on this code and evaluate our prototype atop a HDFS cluster testbed with 21 nodes. As shown in this paper, the recovery bandwidth achieves minimum approximately during recovery of both data block and parity block with STORE. Another attractive result is that the recovery disk I/O also achieves minimum approximately during recovery of data block. Due to the reduction of recovery bandwidth and disk I/O, the degraded read throughput is boosted notably.
机译:最近,诸如Reed-Solomon(RS)码之类的传统擦除码已越来越多地部署在许多分布式存储系统中,以减少由于广泛采用的复制方案而导致的大存储开销。但是,在丢失或不可用的数据恢复期间,这些代码在网络带宽和磁盘I / O方面需要非常高的资源。这称为恢复问题。在本文中,我们致力于将精确的最小带宽重新生成代码集成到实际系统中,以解决恢复问题。我们使用最近提出的BASIC框架和ZigZag可解码代码设计了易于实现的存储代码,以节省恢复带宽和磁盘I / O。我们基于此代码构建了一个名为STORE的系统,并在具有21个节点的HDFS集群测试平台上评估了我们的原型。如本文所示,在使用STORE恢复数据块和奇偶校验块的过程中,恢复带宽大约达到最小。另一个吸引人的结果是,恢复磁盘的I / O大约在恢复数据块期间也达到了最小值。由于恢复带宽和磁盘I / O的减少,显着提高了读取吞吐量的下降。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号