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的群集平台会带来令人振奋的新可能性。然而，虚拟环境中的I/O效率，特别是关于磁盘I/O的效率，仍然鲜为人知，也几乎没有经过测试。这项研究的目标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，然后优化系统以减少此类瓶颈的影响。这项研究将对了解虚拟I/O、识别I/O瓶颈和优化I/O有很大的帮助，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让代表不足的群体参与，从而促进学生在科学和工程领域的职业发展，从而为社会做出贡献。