Collaborative Research: CSR---SMA+AES: PROGNOSIS to Enhance the Runtime Health of Large Scale Parallel Systems

大规模并行系统对于我们的计算基础设施至关重要，它可以应对规模和需求超过当今市场上可用机器能力的应用程序所带来的挑战。推动硬件和软件技术的极限以获得最大的性能，反过来又加剧了其他问题。这些问题中值得注意的是对故障的敏感性，这是由于硬件瞬态错误、硬件设备故障、软件复杂性以及并行系统节点之间复杂的硬件/软件相互依赖关系而引起的。除了影响维护/操作成本外，这些故障还会对系统性能产生重大影响，从而使部署这些大规模系统的动机面临风险。这项研究有望在开发运行时基础设施方面做出三个广泛的贡献，称为预后，用于故障数据收集和在线分析。第一组贡献将是在一段较长时间内从实际的BlueGene/L系统收集和分析系统事件和故障数据。除了呈现原始系统事件外，研究还将开发过滤技术，以删除不重要的信息并识别平稳间隔，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发展的故障数据的模型，通过利用系统事件之间随时间的相关性，跨节点，以及关于外部因素（如强加的工作负载和操作温度）。第三组贡献将是展示预后的用途。像PROGNOSIS这样的工具可以极大地帮助开发自我修复系统，这已经被一些计算机供应商指出是自主计算新兴领域的一个重要目标。

Large scale parallel systems are critical to our computational infrastructure to take on the challenges imposed by applications whose scale and demands exceed the capabilities of machines available in the market today. Pushing the limits of hardware and software technologies to extract the maximum performance, in turn, exacerbates other problems. Notable amongst these problems is the susceptibility to failures, which arises as a consequence of growing hardware transient errors, hardware device failures, software complexity, and the complex hardware/software inter-dependencies between the nodes of a parallel system. These failures can have substantial consequences on system performance, in addition to impacting the costs of maintenance/operation, thereby putting at risk the very motivation behind deploying these large scale systems.This research 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, the research 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. Tools such as PROGNOSIS can help substantially in the development of self-healing systems, which has been noted to be an important goal in the emerging area of Autonomic Computing by several computer vendors.