CAREER: Enable Robust Virtualized Hosting Infrastructures via Coordinated Learning, Recovery, and Diagnosis

大规模虚拟化托管基础设施已经成为许多现实世界系统（如云计算基础设施、企业数据中心、教育计算平台和数据密集型计算系统）的基础平台。然而，托管基础设施由于其固有的复杂性和共享性，容易因软件错误或硬件故障而导致各种性能异常。本提案的总体目标是系统地探索大规模虚拟化托管基础设施的创新运行时可靠性管理技术。我们的研究重点是处理分布式系统中的性能异常，这些异常通常很难离线重现。PI计划结合在线学习、知识驱动的第一反应恢复和现场诊断的力量，以更有效地处理意外的系统异常。她的目标是将运行时系统异常管理从一个试错的猜测游戏转变为一个有效的知识驱动的自我修复过程。本项目开发的技术将对提高许多现实世界托管基础设施系统的可靠性和可诊断性产生重大影响。PI计划基于本项目开发的研究原型开发新的课程模块和编程课件。在这个项目中开发的原型将应用于NCSU的虚拟计算实验室，这是一个面向K-12、社区学院和大学的教育计算平台。她还将通过出版物和技术转让广泛传播她的成果和收集的数据。开发的软件工件和实验数据集也将发布供公众使用。

Large-scale virtualized hosting infrastructures have become the fundamental platforms for many real world systems such as cloud computing infrastructures, enterprise data centers, educational computing platforms, and data-intensive computing systems. However, due to their inherent complexity and sharing nature, hosting infrastructures are prone to various performance anomalies caused by software bugs or hardware failures. The overarching objective of this proposal is to systematically explore innovative runtime reliability management techniques for large-scale virtualized hosting infrastructures. Our research focuses on handling performance anomalies in distributed systems that are often very difficult to reproduce offline. The PI plans to combine the power of online learning, knowledge-driven first-response recovery, and in-situ diagnosis to handle unexpected system anomalies more efficiently and effectively. She aims at transforming the runtime system anomaly management from a trial-and-error guessing game into an efficient knowledge-driven self-healing process. Techniques developed in this project will generate significant impact on improving the reliability and diagnosability of many real world hosting infrastructure systems. The PI plans to develop new course modules and programming courseware based on the research prototype developed in this project. The prototype developed in this project will be applied to the Virtual Computing Lab at NCSU, an educational computing platform for K-12, community colleges and universities. She will also disseminate her results and collected data broadly through publication and technology transfer. Developed software artifacts and experimental datasets will also be released for public use.