HECURA: Toward Automated Problem Analysis of Large Scale Storage Systems

HECURA：迈向大规模存储系统的自动化问题分析

• 批准号: 0621508
• 负责人: Priya Narasimhan
• 金额: $ 99万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-15 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0621508&HistoricalAwards=false
• 关键词: HECURA; Toward; Automated; Problem; Analysis

CMU proposes to explore methodologies and algorithms for automating analysis of failures and performance degradations in large-scale storage systems. Problem analysis includes such crucial tasks as identifying which component(s) misbehaved, likely root causes, and supporting evidence for any conclusions. Combining statistical tools with appropriate instrumentation, we hope to dramatically reduce the difficulty of analyzing performance and reliability problems in deployed storage systems. Such tools, integrated with automated reaction logic, also provide an essential building block for the longer-term goal of self-healing.Automating problem analysis is crucial to achieving cost-effective storage at the scales needed for tomorrow's high-end computing systems. The number of hardware and software components will make problems common rather than anomalous, so it must be possible to quickly move from problem to fix with little-to-no system downtime for analysis. Further, the distributed software complexity of such systems make by-hand analysis increasingly untenable. More nuanced, but perhaps of most concern, implementors of these storage systems are increasingly unable to test in representative high-end computing environments because they simply cannot afford to recreate the necessary system scale. As a result, scale-related problems must be analyzed in the field to allow improvements to be made, which introduces delays and productivity reductions for customers/users plus issues of clearance for systems deployed to support highly sensitive activities. Current designs and tools fall far short of what is needed.

CMU提出探索在大规模存储系统中自动分析故障和性能下降的方法和算法。问题分析包括这样一些关键的任务，如确定哪些组件行为不当，可能的根本原因，以及支持任何结论的证据。将统计工具与适当的仪器相结合，我们希望大大降低分析已部署存储系统的性能和可靠性问题的难度。这些工具与自动反应逻辑集成在一起，还为实现自我修复的长期目标提供了必要的构建块。自动化问题分析对于实现未来高端计算系统所需规模的经济高效存储至关重要。硬件和软件组件的数量将使问题变得常见，而不是异常，因此必须能够快速地从问题转移到修复，几乎不需要系统停机时间进行分析。此外，这种系统的分布式软件复杂性使得手工分析越来越站不住脚。更微妙的是，这些存储系统的实现者越来越无法在具有代表性的高端计算环境中进行测试，因为他们根本负担不起重新创建必要的系统规模。因此，必须在现场分析与规模有关的问题，以便进行改进，这给客户/用户带来了延迟和生产力降低，以及为支持高度敏感活动而部署的系统的许可问题。目前的设计和工具远远不能满足需要。