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

近年来，在数据密集型计算中出现了由数十万个硬盘组成的大规模数据集群。由于磁盘故障可能导致宝贵数据或信息的丢失，因此磁盘故障的直接和间接成本在这些计算平台的部署和操作中日益成为关键问题。最近的研究得出结论，数据密集型计算的趋势是，即使在运行的头几年，现场的磁盘替换率也比制造商数据表的平均故障时间估计值要高得多。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优和接近最优的并行性布局，例如小规模存储架构中的分散式奇偶校验组织。基于多路复制的存储体系结构与基于erasure code的存储体系结构同样重要，但有很大的不同，关于这方面的研究很少。此外，由于目前的理想位置解决方案的配置数量有限，因此很难扩展到大尺寸。最后，快速恢复需要有效的反向数据查找，这在当前可扩展的数据分布方案中没有得到很好的研究。研究人员开发了实现快速恢复的方法和工具，通过利用最优和接近最优的并行技术，以及分布式哈希表和反向哈希技术来提高高性能存储系统中反向数据查找的可扩展性。如果研究成功，将提供一种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。

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.