Research on Very Large Secondary Storage System based on Multiple Disk-Array and its Functional Management Mechanism

在本研究中，我们考虑一个非常大的二级存储系统配置，它由多个磁盘阵列组成，通过适当的网络连接，即阵列磁盘阵列系统。在第一年，我们已经提出了新的磁盘阵列架构，如虚拟条带化、热块集群和热镜像，并基于这些技术开发了一个精细的磁盘阵列模拟器。由于需要非常大的二级存储系统阵列磁盘阵列的应用需要处理大量的数据，因此我们将大事务处理作为我们的阵列磁盘阵列的目标应用，并检索运行在商用RDB上的TPC-C的跟踪数据。然后，通过分析跟踪数据来估计系统的性能。第二年，我们将大型数据屋作为系统的目标应用，检索运行在商用RDB上的TPC-D的跟踪数据。我们利用上述跟踪数据分析了超大型数据感知屋的访问位置。考虑到各种索引机制，特别是多列索引及其元数据管理，提出了基于应用程序估计的数据特征的自包含性能调优机制。在此基础上，提出了利用元数据信息对阵列磁盘阵列进行预取的机制。通过对事务应用程序（TPC-C）和数据感知库应用程序（TPC-D）等大型数据库应用程序的分析，对所提方法进行了性能评价。结果，我们证明了我们方法的有效性。

In this research, we consider a very large secondary storage system configuration which consists of multiple disk arrays connected by appropriate network, that is, an arrayed disk-array system.At the first year, We have already proposed novel disk array architecture, such as virtual striping, hot block clustering and hot mirroring and developed a fine disk array simulator based on these techniques. Since the applications which require a very large secondary storage system, the arrayed disk-array, process a large amount of data, we took large transaction processing as a target application of our arrayed disk-array and retrieve trace data of TPC-C running on commercial RDB.Then, we estimate the performance of our system analyzing the trace data.At the second year, we took large data houses as a target application of our system and retrieve trace data of TPC-D running on commercial RDB.We analyzed the access locality of very large dataware houses by using the above trace data. Considering various index mechanisms, especially multicolumn index and its metadata management, we proposed the self-contained performance tuning mechanism based on data characteristics which estimated by applications. Moreover, the prefetch mechanism on the arryaed disk-array by utilizing metadata information was proposed. The performance evaluation of these proposed method was done by analyzing large database applications, such as transaction application (TPC-C) and dataware house application (TPC-D). Consequently, we showed the efficiency of our methods.