RUI: Automatic Identification of I/O Bottleneck and Run-time Optimization for Cluster Virtualization

RUI：集群虚拟化I/O瓶颈自动识别与运行时优化

• 批准号: 1102624
• 负责人: Xubin He
• 金额: $ 22.81万
• 依托单位: Virginia Commonwealth University
• 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: 2010-09-03 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1102624&HistoricalAwards=false
• 关键词: RUI; Automatic; Identification; Bottleneck; Run

Extending virtualization technology into high-performance, cluster platforms generates exciting new possibilities. However, I/O efficiency in virtualized environments, specifically with respect to disk I/O, remains little understood and hardly tested. The objective of this research is to investigate fundamental techniques for virtual clusters that not only facilitate rigorous performance studies, but also identify places where performance is suffering and then optimize the system to lessen the impact of such bottlenecks. This research will greatly contribute to understanding virtualized I/O, identifying I/O bottlenecks and optimizing I/O, and thus facilitate the cluster systems to most effectively utilize virtualization technology. This project will also contribute to the society through promoting research and engaging under-represented groups that leads students to advancing their careers in science and engineering.

将虚拟化技术扩展到高性能集群平台会产生令人兴奋的新可能性。然而，虚拟化环境中的 I/O 效率，特别是磁盘 I/O 的效率，仍然知之甚少，也几乎没有经过测试。本研究的目的是研究虚拟集群的基本技术，这些技术不仅可以促进严格的性能研究，还可以识别性能受到影响的地方，然后优化系统以减轻此类瓶颈的影响。这项研究将极大地有助于理解虚拟化I/O、识别I/O瓶颈和优化I/O，从而促进集群系统最有效地利用虚拟化技术。该项目还将通过促进研究和吸引代表性不足的群体来为社会做出贡献，从而引导学生在科学和工程领域取得职业发展。