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

大规模并行系统对我们的计算基础设施至关重要，以应对应用程序带来的挑战，这些应用程序的规模和需求超出了当今市场上可用机器的能力。挑战硬件和软件技术的极限以获取最高性能，反过来又会加剧其他问题。在这些问题中值得注意的是对故障的敏感性，这是由于并行系统的节点之间日益增长的硬件瞬时错误、硬件设备故障、软件复杂性以及复杂的硬件/软件相互依赖而引起的。这些故障除了影响维护/操作成本外，还可能对系统性能产生重大影响，从而危及部署这些大规模系统的动机。这项研究预计将对开发称为预测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实际Bluegene/L系统在较长一段时间内的系统事件和故障数据。除了提供原始系统事件外，研究还将开发过滤技术，以删除不重要的信息和识别平稳间隔，并定义记录的属性及其频率。第二组贡献将是用于在线分析和预测不断演变的故障数据的模型，方法是利用系统事件随时间推移、跨节点以及与外部因素(如施加的工作负载和运行温度)之间的相关性。第三组贡献将是关于展示预测的用途。像预测这样的工具可以在很大程度上帮助自我修复系统的开发，这已经被几个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.