CSR-PSCE,SM: Recovery Aware Parallel Computing

CSR-PSCE,SM：恢复感知并行计算

• 批准号: 0834514
• 负责人: Zhiling Lan
• 金额: $ 33万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0834514&HistoricalAwards=false
• 关键词: CSR; PSCE; SM; Recovery; Aware; CSR; PSCE; SM; Recovery; Aware

As the scale and complexity of parallel systems continue to grow, failures are inevitable. For years research focused on pre-failure prediction and tolerance - predicting failures and taking precautionary actions before failure occurrence. Despite progress on failure prediction, unexpected failures occur in practice, especially in modern systems with unprecedented sizes and complexities. Relying on pre-failure prediction and tolerance alone is insufficient for fault management because of the inevitability of failures. Just as failures need to be carefully avoided and managed when they occur, post-failure diagnosis and recovery is of equal importance and has a profound impact on almost every aspect of parallel computing. The goal of this research project is to develop RAPS, a Recovery Aware Parallel computing System for post-failure diagnosis and recovery. The research focuses on how to quickly and effectively resume parallel computing after a failure has occurred. The ultimate goal is to seamlessly integrate post-failure diagnosis and recovery with pre-failure prediction and tolerance as a compound fault management solution for parallel computing. The approach consists of (1) development of new diagnosis mechanisms for fast failure detection and root cause analysis, (2) development of system-wide orchestration for recovery coordination, (3) design of new recovery techniques for quick restoration of parallel applications, and (4) a comprehensive evaluation. The results of this project can significantly improve the productivity of parallel systems. This project also enhances the CS curriculum at IIT and broadens the participation by underrepresented groups.

随着平行系统的规模和复杂性不断增长，失败是不可避免的。 多年来，研究重点是预测预测和宽容 - 预测失败并在发生故障之前采取预防措施。尽管在失败预测方面取得了进展，但实践中会出现意外的失败，尤其是在具有前所未有的大小和复杂性的现代系统中。由于失败的必然性，仅依靠前失败的预测和容忍度不足以进行故障管理。 正如发生故障时需要仔细避免和管理故障一样，失败后的诊断和恢复也具有同等的重要性，并且对平行计算的几乎每个方面都有深远的影响。 该研究项目的目的是开发说唱，这是一种恢复意识到的并行计算系统，用于失败后诊断和恢复。该研究的重点是如何在发生故障后快速有效地恢复并行计算。最终的目标是将失败后的诊断和恢复与失败预测和公差无缝整合，作为用于并行计算的复合故障管理解决方案。该方法包括（1）开发用于快速失败检测和根本原因分析的新诊断机制，（2）开发全系统范围的恢复协调协调，（3）设计新的恢复技术以快速恢复并行应用，以及（4）全面评估。该项目的结果可以显着提高并行系统的生产率。该项目还增强了IIT的CS课程，并扩大了代表性不足的群体的参与。