CSR:Small: Online System Anomaly Prediction and Diagnosis for Large-Scale Hosting Infrastructures

CSR:Small：大规模托管基础设施的在线系统异常预测与诊断

• 批准号: 0915567
• 负责人: Xiaohui Gu
• 金额: $ 40.5万
• 依托单位: North Carolina State 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=0915567&HistoricalAwards=false
• 关键词: CSR; Small; Online; System; Anomaly

Large-scale hosting infrastructures have become important platforms for many real-world systems such as cloud computing, enterprise data centers, massive data analytics, and web hosting services. Unfortunately, today's large-scale hosting infrastructures are still vulnerable to various system anomalies such as performance bottlenecks, resource hotspots, service level objective (SLO) violations, and various software/hardware failures. The goal of this project is to assess the viability of an online predictive anomaly management solution for large-scale hosting infrastructures. We will develop novel techniques for 1) performing light-weight online system anomaly prediction; 2) providing self-evolving anomaly prediction models to achieve high-quality prediction for real-world dynamic systems; and 3) performing speculative, ``hot" system anomaly diagnosis that search possible anomaly causes and suggest corrective actions while the system approaches the anomaly state. Our research will carry out evaluation by conducting experiments and case studies with our industrial partners on realistic platforms. Students supported by this project will gain experience with development and testing of robust real-world hosting infrastructures through interactions with our industrial partners, through internships and onsite experimentation. This work will advance diversity by involving students from under-represented groups. Particularly, the prototype developed in this project will be applied to the Virtual Computing Lab (VCL) at NCSU, a platform for providing a better educational experience for K-12, community colleges, and universities. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

大规模托管基础设施已成为云计算、企业数据中心、海量数据分析和网络托管服务等许多现实系统的重要平台。不幸的是，当今的大规模托管基础设施仍然容易受到各种系统异常的影响，例如性能瓶颈、资源热点、服务级别目标（SLO）违规以及各种软件/硬件故障。该项目的目标是评估大规模托管基础设施的在线预测异常管理解决方案的可行性。我们将开发新技术：1）执行轻量级在线系统异常预测； 2）提供自演化的异常预测模型，实现对现实世界动态系统的高质量预测； 3）执行推测性的“热”系统异常诊断，在系统接近异常状态时搜索可能的异常原因并建议纠正措施。我们的研究将通过在现实平台上与工业合作伙伴进行实验和案例研究来进行评估。受该项目支持的学生将通过与我们的工业合作伙伴的互动、实习和现场实验来获得开发和测试强大的现实世界托管基础设施的经验。这项工作将通过让来自代表性不足的群体的学生参与来促进多样性。发达该项目的奖金将应用于北卡罗来纳州立大学的虚拟计算实验室 (VCL)，该实验室是一个为 K-12、社区学院和大学提供更好教育体验的平台。该奖项由 2009 年美国复苏和再投资法案 (American Recovery and Reinvestment Act of 2009) 资助。公法 111-5）。