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: 1048432
• 负责人: Mais Nijim
• 金额: $ 10万
• 依托单位: Texas A&M University-Kingsville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048432&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提案＃0917137CSR：小：协作研究：FastSTOR：基于数据挖掘的Mmultilayer预取，用于混合存储Systemsabtracta的大量现有并行存储系统由混合存储组成，包括固态驱动器（SSD），硬盘（HDD）和Tapes和Tapes。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打击速率来提高I/O性能的通常使用的技术。但是，由于有趣的困境，在混合存储系统的背景下进行预摘要在技术上具有挑战性：积极的预取方案可以有效地降低I/O潜伏期，而过度累积的方案可能会浪费I/O带宽来通过将无用数据从HDDS转移到SSD或从SSD或从磁带转移到HDDS来浪费I/O带宽。在这个名为FastStor的研究项目中，我们研究了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。这项研究的目标是（1）设计数据挖掘算法的多层预取算法； （2）为基于SSD的存储系统开发预测的并行预取机制； （3）在SSD，HDD和磁带之间实现并行数据传输； （4）制定元数据管理方案； （5）实施一个名为faststor-sim的仿真框架。开发的工具包可用于通过混合存储系统提高数据中心的I/O性能。该项目的研究结果发表在会议或期刊上，以获取公众知识。通过奥本大学，南达科他州矿业与技术学院的合作，以及南密西西比大学，PIS通过使研究生和本科生将学习和培训促进学习和培训。