CSR: Small: Collaborative Research: FastStor: Data-Mining-Based Multilayer Prefetching for Hybrid Storage Systems

CSR：小型：协作研究：FastStor：混合存储系统基于数据挖掘的多层预取

• 批准号: 0917137
• 负责人: Xiao Qin
• 金额: $ 20万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917137&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; FastStor

CSR proposal #0917137CSR:Small:Collaborative Research: FastStor: Data-Mining-BasedMultilayer Prefetching for Hybrid Storage SystemsAbstractA large number of existing parallel storage systems consist of hybrid storage components, including solid-state drives (SSD), hard disks (HDD), and tapes. Compared with high-speed storage components (e.g. SSD and HDD), tapes inevitably become an I/O performance bottleneck. Prefetching and caching are commonly employed techniques to boost I/O performance by increasing the data hitting rate of high-end storage components. However, prefetching in the context of hybrid storage systems is technically challenging due to an interesting dilemma: aggressive prefetching schemes can efficiently reduce I/O latency, whereas overaggressive schemes may waste I/O bandwidth by transferring useless data from HDDs to SSDs or from tapes to HDDs. In this research project, called FastStor, we investigate new data-mining-based multilayer prefetching techniques to improve performance of hybrid storage systems. The goals of this research are to (1) design data-mining algorithms for multilayer prefetching; (2) develop predictive parallel prefetching mechanism for SSD-based storage systems; (3) implement parallel data transfer among SSDs, HDDs, and tapes; (4) develop meta-data management schemes; and (5) implement a simulation framework named FastStor-SIM. The developed toolkit can be used to improve the I/O performance of data centers with hybrid storage systems. The research findings of this project are published in conferences or journals for public knowledge. Through the collaboration of Auburn University, South Dakota School of Mines and Technology, and the University of Southern Mississippi, PIs promote learning and training by exposing graduate and undergraduate students to technological underpinnings in the fields of storage systems.

CSR proposal #0917137 CSR：Small：Collaborative Research：FastCache：Data-Mining-Based Multilayer Prefetching for Hybrid Storage SystemsAbstract大量现有的并行存储系统由混合存储组件组成，包括固态驱动器（SSD）、硬盘（HDD）和磁带。与高速存储组件（例如SSD和HDD）相比，磁带不可避免地成为I/O性能瓶颈。 预取和缓存是通过提高高端存储组件的数据命中率来提高I/O性能的常用技术。然而，在混合存储系统的上下文中的预取是技术上具有挑战性的，由于一个有趣的困境：积极的预取方案可以有效地减少I/O延迟，而过于积极的计划可能会浪费I/O带宽，从硬盘到SSD或从磁带到硬盘传输无用的数据。在这个研究项目中，被称为Fastfixed，我们研究新的基于数据挖掘的多层预取技术，以提高混合存储系统的性能。本研究的目标是：（1）设计多层预取的数据挖掘算法;（2）开发基于SSD的存储系统的预测并行预取机制;（3）实现SSD、HDD和磁带之间的并行数据传输;（4）开发元数据管理方案;（5）实现一个名为FastStor-SIM的仿真框架。开发的工具包可用于提高混合存储系统的数据中心的I/O性能。该项目的研究成果发表在会议或期刊上，供公众了解。通过与奥本大学、南达科他州矿业与技术学院和南密西西比大学的合作，PI通过让研究生和本科生接触存储系统领域的技术基础来促进学习和培训。