CSR/AES: Enhancing Application Robustness via Adaptive and Cooperative Methods

CSR/AES：通过自适应和协作方法增强应用程序的稳健性

• 批准号: 0720549
• 负责人: Zhiling Lan
• 金额: --
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720549&HistoricalAwards=false
• 关键词: CSR; AES; Enhancing; Application; Robustness

As the scale of high performance computing continues to grow, application robustness becomes increasingly important. Checkpointing is the conventional method for fault tolerance. However, it only deals with failures after their occurrence through rollback. In case of one process failure, all processes including non-faulty processes have to be restarted from the previously saved state prior to the failure. Thus, significant performance loss can be incurred due to the work loss and failure recovery. Proactive approaches take preventive actions (e.g. preemptive process migration) before failures, thereby avoiding failures with low cost. Nevertheless, its effectiveness relies on perfect fault prediction, which is hardly achievable in practice. This project investigates a new approach called adaptive fault management by intelligently integrating proactive and reactive robustness techniques such that it will enable applications to avoid anticipated faults if possible, and in the case of unforeseeable faults, to tolerate these faults in such a way that their impact is kept to a minimum. The project consists of three major components: (1) cooperative anomaly diagnosis (CAD) to improve fault prediction in large-scale systems by developing meta-learning methods; (2) adaptive control manager (ACM) to allow runtime decision making in response to imperfect fault prediction; and (3) integrated runtime support (IRS) to enable cost-effective coordination of fault handing techniques at runtime. The resulting framework will enhance robustness of high performance computing applications by improving their performance in the presence of failures. This project also enhances the systems-area curriculum at Illinois Institute of Technology and helps train the future-generation scientific computing workforce.

随着高性能计算规模的不断增长，应用程序的健壮性变得越来越重要。检查点是容错的常规方法。但是，它只在故障发生后通过回滚处理故障。在一个进程失败的情况下，包括非故障进程在内的所有进程都必须从故障之前的先前保存状态重新启动。因此，由于工作损失和故障恢复，可能会导致显著的性能损失。主动方法在故障发生前采取预防措施（例如，先发制人的流程迁移），从而以低成本避免故障。然而，它的有效性依赖于完美的故障预测，这在实践中很难实现。该项目研究了一种称为自适应故障管理的新方法，通过智能地集成主动和被动鲁棒性技术，使应用程序能够在可能的情况下避免预期的故障，并且在不可预见的故障的情况下，以这种方式容忍这些故障，使其影响保持在最小。该项目包括三个主要组成部分：(1)协同异常诊断（CAD），通过开发元学习方法来改进大规模系统的故障预测；(2)自适应控制管理器（ACM），允许在不完全故障预测下进行运行时决策；(3)集成运行时支持（IRS），以实现运行时故障处理技术的经济高效协调。由此产生的框架将通过改进故障情况下的性能来增强高性能计算应用程序的健壮性。该项目还加强了伊利诺伊理工学院系统领域的课程，并有助于培养下一代科学计算人才。