CSR: Medium: Collaborative Research: Wizard: Exploiting Disk Performance Signatures for Cost-Effective Management of Large-Scale Storage Systems

CSR：中：协作研究：向导：利用磁盘性能签名实现大规模存储系统的经济高效管理

• 批准号: 1563750
• 负责人: Song Fu
• 金额: $ 30万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563750&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; Wizard

The tremendous advances in low-cost, high-capacity magnetic hard disk drives, flash-based solid state drives and non-volatile memory have been among the key factors supporting big data applications and various computing-storage services that the modern society deeply relies on. However, storage drives are reported to be the most commonly replaced hardware components because of failures. This causes service downtime and even data loss, costing enterprises multi-trillion dollars per year. Existing disk failure management approaches are mostly reactive and incur high overheads; they do not provide a cost-effective solution to managing large-scale production storage systems. The goal of this project is to achieve a deep understanding of the reliability of the real-world storage systems, and to develop a cost-effective data and storage resource management system for reliability enhancement. The investigators' approach to building reliable, large-scale storage systems is carefully designed to support storage health monitoring, modeling, prediction and proactive recovery in a systematic fashion. In particular, they first categorize and model storage failures to derive disk performance signatures and explore disk performance signatures to forecast occurrences of disk failures. They then characterize I/O workload dependency in disk performance degradation and integrate the performance signatures of heterogeneous disk devices to effectively reconfigure and manage storage resources. Furthermore, the project will provide easy-to-use APIs for storage users and developers to employ the developed tools and techniques for proactive data rescue and preventive disk reliability enhancement. Finally, the project provides excellent opportunities for training graduate students, especially minority and female students, and for developing new curriculum materials on reliable storage systems.

低成本、大容量硬盘驱动器、闪存固态硬盘和非易失性存储器的巨大进步已经成为支撑现代社会所依赖的大数据应用和各种计算存储服务的关键因素之一，但据报道，存储驱动器是最常因故障而被替换的硬件组件。这会导致服务停机甚至数据丢失，每年给企业造成数万亿美元的损失。现有的磁盘故障管理方法大多是被动的，并会产生很高的开销，他们不提供一个具有成本效益的解决方案来管理大规模的生产存储系统。该项目的目标是深入了解现实世界的存储系统的可靠性，并开发一个具有成本效益的数据和存储资源管理系统，以提高可靠性。研究人员构建可靠的大规模存储系统的方法经过精心设计，以系统的方式支持存储健康监控，建模，预测和主动恢复。特别是，他们首先对存储故障进行分类和建模，以获得磁盘性能特征，并探索磁盘性能特征，以预测磁盘故障的发生。然后，他们在磁盘性能下降的I/O工作负载的依赖性特征，并集成异构磁盘设备的性能签名，以有效地重新配置和管理存储资源。此外，该项目将为存储用户和开发人员提供易于使用的API，以使用开发的工具和技术进行主动数据救援和预防性磁盘可靠性增强。最后，该项目为培训研究生，特别是少数民族学生和女生，以及为编写关于可靠储存系统的新教材提供了极好的机会。