CSR: Small: Monitoring for Error Detection in Today's High Throughput Applications

CSR：小：监控当今高吞吐量应用程序中的错误检测

• 批准号: 0916337
• 负责人: Saurabh Bagchi
• 金额: $ 25.9万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916337&HistoricalAwards=false
• 关键词: CSR; Small; Monitoring; Error; Detection

CSR: Small: Monitoring for Error Detection in Today?s High Throughput ApplicationsAbstract: Much of our critical infrastructure is formed by distributed systems with real-time requirements. Downtime of a system providing critical services in power systems, air traffic control, banking, and railways signaling could be catastrophic. The errors may come from individual software components, interactions between multiple components, or misconfiguration of these components. It is therefore imperative to build low latency detection systems that can subsequently trigger the diagnosis and recovery phases leading to systems that are robust to failures. A powerful approach for error detection is the stateful approach, in which the error detection system builds up state related to the application by aggregating multiple messages. The rules are then based on the state, thus on aggregated information rather than on instantaneous information. Though the merits of stateful detection seem to be well accepted, it is difficult to scale stateful detection with an increasing number of application components or increasing data rate. This is due to the increased processing load of tracking application state and rule matching based on the state. In this project, we address this issue through designing a runtime monitoring system focused on high throughput distributed applications. Our solution is based on intelligent sampling, probabilistic reasoning on the application state, and opportunistic monitoring of the heavy-duty rules. A successful solution will allow reliable operation of high bandwidth distributed applications and those with a large number of consumers. We will also achieve broader impact through an innovative service learning program at Purdue called EPICS and a new course.

CSR：小：今天的错误检测监测？s高吞吐量应用摘要：我们的许多关键基础设施是由具有实时要求的分布式系统组成的。在电力系统、空中交通管制、银行和铁路信号中提供关键服务的系统的停机可能是灾难性的。错误可能来自单个软件组件、多个组件之间的交互或这些组件的错误配置。因此，必须构建低延迟检测系统，该系统随后可以触发诊断和恢复阶段，从而导致对故障具有鲁棒性的系统。一种强大的错误检测方法是有状态方法，在这种方法中，错误检测系统通过聚合多个消息来构建与应用程序相关的状态。然后，规则基于状态，因此基于聚合信息而不是瞬时信息。虽然状态检测的优点似乎被广泛接受，但随着应用程序组件数量的增加或数据速率的增加，很难扩展状态检测。这是由于跟踪应用程序状态和基于状态的规则匹配的处理负载增加。在这个项目中，我们解决这个问题，通过设计一个运行时监控系统集中在高吞吐量的分布式应用程序。我们的解决方案是基于智能采样，概率推理的应用程序的状态，并伺机监视重型规则。一个成功的解决方案将允许高带宽分布式应用程序和具有大量消费者的应用程序的可靠运行。我们还将通过普渡大学的创新服务学习计划EPICS和一门新课程来实现更广泛的影响。