Scalable Self-Healing Systems

当今的企业信息系统包括软件、计算机、网络等的大型、复杂的互连组合。这些系统是使命关键的，并且因此期望是高度可用的，提供良好的性能，并且在有限的预算内操作。 目前，这是通过让高可用性数据中心管理员主动监控关键服务、识别故障、服务级别协议（SLA）违规等来实现的，定位和诊断故障组件，并通常确保系统处于正确的运行状态。 这种方法是昂贵的（美国每年的商业成本约为1000亿美元，到2018年将增长30%;美国劳工统计局）。 虽然最近的研究和行业关注使这样的系统自我修复，但当前的技术要求软件组件是简单的、无状态的和仅崩溃的。 这些要求是很难达到与传统的企业信息系统，此外，目前的方法只考虑一小部分非常通用的自动恢复操作。 为了解决这些限制，需要精确的故障定位和诊断方法，以及复杂的错误恢复算法。 本研究的目的是设计这种新的算法和方法，并将它们集成在一个系统管理基础设施，从而创建一个自我修复的元服务器，它将提供端到端的错误检测，故障定位和诊断，以及错误恢复，并在数据中心规模这样做。 我们的方法是创建可扩展的，运行时的行为模型的系统的正常运行，并确定系统行为和运行时模型预测之间的偏差。 任何这样的异常通常都是由工作负载更改、系统更改或系统故障引起的;我们可以监视工作负载和系统更改，因此这两个更改都不会被识别出来，而是假定发生了故障，并启动恢复。 鉴于企业信息系统的运行成本很高，我们的研究预计将在降低成本方面具有非常重要的意义。

Enterprise information systems today comprise large, complex interconnected assemblages of software, computers, networks, etc. These systems are mission critical, and as such are expect to be highly available, offer good performance, and operate within constrained budgets. Currently, this is achieved by having highly available datacenter administrators actively monitor critical services, identifying failures, service-level agreement (SLA) violations, etc., localizing and diagnosing faulty components, and generally ensuring that the system is in a correctly functioning state. This approach is expensive (the cost to business in the US is approximately $100 billion annually, rising 30% by 2018; US Bureau of Labor Statistics). While there has been recent research and industry attention given to having such systems self-heal, current techniques require the software components to be simple, stateless, and crash-only. These requirements are hard to attain with legacy enterprise information systems, Furthermore, current approaches consider only a small set of very generic automated recovery actions. To address these limitations, accurate fault localization and diagnosis methods, together with sophisticated error-recovery algorithms, are required. It is the objective of this research to devise such novel algorithms and methods, and to integrate them within a systems-management infrastructure, so as to create a self-healing meta-server which will provide end-to-end error detection, fault localization and diagnosis, and error recovery, and to do so at the datacenter scale. Our approach is to create scalable, run-time behavioural models of the normal operation of the system, and to identify deviations between system behaviour and run-time-model prediction. Any such anomalies are typically caused by workload change, system change, or system fault; we can monitor workload and system change, and so neither of those are identified, a fault is presumed, and recovery initiated. Given the large costs of running enterprise information systems, our research is expected to be very significant in cost reduction.