CPA-ACR: Fast Recovery Using Optimal and Near-Optimal Parallelism in Data-Intensive Computing

CPA-ACR：在数据密集型计算中使用最优和近最优并行性进行快速恢复

• 批准号: 0811413
• 负责人: Jun Wang
• 金额: $ 27.4万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811413&HistoricalAwards=false
• 关键词: CPA; ACR; Fast; Recovery; Using

Recent years have seen the emergence of large-scale data clusters consisting of hundreds of thousands hard drives in data-intensive computing. Because failure of a disk could cause loss of valuable data or information, the direct and indirect costs of ever often disk failures are becoming increasingly critical issues in the deployment and operation of these computational platforms. Recent studies conclude that the trend in data-intensive computing is towards much higher disk replacement rates in the field than the Mean-Time-To-Failure estimates of the manufacturer datasheet would suggest, even in the first years of operation. Hence, the recovery becomes state of storage. Existing storage recovery solutions successfully developed optimal and near-optimal parallelism layouts such as declustered parity organizations at small-scale storage architectures. There are very few studies on multi-way replication based storage architectures that are equally important but significantly different from erasure code based storage architectures. Moreover, it is difficult to scale up to a large size because current placement-ideal solutions have a limited number of configurations. Lastly, fast recovery demands efficient reverse data lookup, which is not well studied in current scalable data distribution schemes. The investigators develop methods and tools for achieving fast recovery by exploiting optimal and near-optimal parallelism techniques, and distributed hash table and reverse hashing techniques to improve the scalability of reverse data lookup in high-performance storage systems. The proposed research, if successful, will have broad impact in both fault-tolerance computing and high-performance computing community by providing a scalable and fast storage recovery solution.

近年来，在数据密集型计算中出现了由数十万个硬盘驱动器组成的大规模数据集群。由于磁盘的故障可能会导致有价值的数据或信息的丢失，因此经常发生的磁盘故障的直接和间接成本正在成为这些计算平台的部署和操作中越来越重要的问题。最近的研究得出结论，数据密集型计算的趋势是，即使在运行的第一年，现场的磁盘更换率也比制造商的平均故障时间估计值高得多。因此，恢复成为存储状态。现有的存储恢复解决方案成功地开发了最佳和接近最佳的并行布局，例如在小规模存储架构中的分散奇偶校验组织。基于多路复制的存储体系结构与基于纠删码的存储体系结构同样重要，但却有着显著的不同。此外，难以按比例放大到大尺寸，因为当前的放置理想解决方案具有有限数量的配置。最后，快速恢复需要高效的反向数据查找，这在当前的可扩展数据分发方案中没有得到很好的研究。研究人员开发的方法和工具，通过利用最佳和接近最佳的并行技术，分布式哈希表和反向哈希技术，以提高在高性能存储系统中的反向数据查找的可扩展性，实现快速恢复。该研究如果成功，将通过提供可扩展的快速存储恢复解决方案，在容错计算和高性能计算领域产生广泛的影响。