CSR---AES: Fault Determination and Recovery in Cycle-Sharing Infrastructures

CSR---AES：自行车共享基础设施中的故障确定和恢复

• 批准号: 0509387
• 负责人: Suresh Jagannathan
• 金额: --
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0509387&HistoricalAwards=false
• 关键词: CSR; AES; Fault; Determination; Recovery

This project is expected to make three broad contributions towards developing a runtime infrastructure, called PROGNOSIS, for failure data collection and online analysis. The first set of contributions will be on collecting and analyzing system events and failure data from an actual BlueGene/L system over an extended period of time. In addition to presenting the raw system events, we will be developing filtering techniques to remove unimportant information and identifying stationary intervals, together with defining the attributes for logging and their frequency. The second set of contributions will be models for online analysis and prediction of evolving failure data by exploiting correlations between system events over time, across the nodes, and with respect to external factors such as imposed workload and operating temperature. The third set of contributions will be on demonstrating the uses of PROGNOSIS. This work will be specifically extending two important runtime techniques - parallel job scheduling and checkpointing - with the information provided by PROGNOSIS; will investigate how predictability of failures along spatial and/or temporal dimensions can enhance schedulers to provide a better trade-off between higher system utilization versus job loss upon failures, and will develop techniques to fine tune the frequency and location of checkpoints with PROGNOSIS. More importantly, the confidence level behind the prediction that is needed for online decision making will be evaluated, and the effect of inaccurate predictions.

该项目预计将对开发一个称为PROGNOSIS的运行时基础设施做出三大贡献，用于故障数据收集和在线分析。第一组贡献将是收集和分析系统事件和故障数据，从一个实际的BlueGene/L系统在一个延长的时间段。除了呈现原始系统事件之外，我们还将开发过滤技术来删除不重要的信息并识别固定间隔，同时定义日志记录的属性及其频率。第二组贡献将是在线分析和预测不断变化的故障数据的模型，通过利用系统事件之间的相关性，随着时间的推移，跨节点，以及相对于外部因素，如强加的工作负载和工作温度。第三组贡献将展示PROGNOSIS的使用。这项工作将具体扩展两个重要的运行时技术-并行作业调度和检查点-与PROGNOSIS提供的信息;将研究故障沿着空间和/或时间维度的可预测性如何能够增强制造商以提供更高的系统利用率与故障时的工作损失之间的更好的权衡，并将开发技术来微调频率和检查点的位置与预测。更重要的是，将评估在线决策所需的预测背后的置信水平，以及不准确预测的影响。