负载自适应的存储池数据重构策略

大数据时代各应用领域对计算机存储系统的性能和可靠性需求与日俱增。新型存储介质为计算机存储系统的性能提升提供了良好的机遇，基于固态盘的存储阵列（RAIS）已在各种存储系统中广泛使用。传统RAIS系统中当一块固态盘出现故障时，通过数据重构操作恢复故障盘的数据，重构时间长，且影响对上层应用提供I/O访问服务的能力。针对该问题，设计实现了基于多线程并发处理的存储池架构，该架构能够并发处理存储池中的I/O请求，提高用户I/O和数据重构I/O的访问性能。提出了一种负载自适应的I/O调度策略，能够在保证用户I/O服务质量的同时，提升数据重构效率。实验结果表明，基于存储池的多线程并发I/O处理架构能够提升数据重构性能，负载自适应的I/O调度策略能够根据用户I/O的负载情况动态调整用户I/O和数据重构I/O的调度比例，在保证用户I/O服务质量的同时，提升数据重构效率。

A workload-adaptive data reconstruction strategy in SSD pool

In big data era, various applications bring increasingly higher demands on storage system' capacity, performance and reliability. New storage media brings good opportunity in improving the performance of storage system, and Redundant Array of Independent SSDs (RAIS) has been widely used in various storage systems. Data reconstruction, however, takes long time and affects its ability of provid- ing I/O access services to upper level applications, when a disk fails in RAIS. A storage pool architecture supporting multithreading and concurrent processing is designed and implemented, in which I/O requests evenly distributed in all SSDs can be processed concurrently and the access performance of user I/O and data reconstruction I/O are improved. A workload-adaptive I/O scheduling strategy is proposed, which can guarantee the quality of user I/O service and improve the efficiency of data reconstruction. Experimental results show that the multithreaded concurrent I/O processing architecture based on storage pool can improve data reconstruction performance. The workload-adaptive I/O scheduling strategy can adjust the scheduling ratio of user I/O and data reconstruction I/O dynamically, ensure the service quality of user I/O and improve the efficiency of data reconstruction.