SBIR Phase I: Providing Automatic Anomaly Prediction and Diagnosis Software as a Service for Cloud Infrastructures

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力将是极大地提高许多现实世界云计算基础设施的健壮性和可诊断性。建议的技术将显著减少生产云系统的停机时间，可以吸引更多的用户采用云计算技术，从而造福于依赖云技术的不断扩大的社会阶层和经济。该项目还将通过将研究成果应用于现实世界，促进云系统可靠性研究的最新水平。这个小企业创新研究(SBIR)第一阶段项目将改变生产云计算基础设施的系统异常管理。该公司解决方案的创新之处在于三个独特的功能：1)它提供了自动多变量异常检测，可以实现高保真异常警报，而不会给用户带来任何配置负担；2)它在大系统问题发生之前提供早期异常警报；3)它提供异常诊断，可以生成异常发生的提示。拟议的研究将产生新的、实用的异常预测和诊断解决方案，并将在现实世界的云基础设施中进行验证。具体地说，该项目由两个部分组成：1)在线多变量异常预测，探索新的轻量级无监督学习算法，以实现高保真异常警报并提供故障时间估计；2)自动异常诊断，可以识别异常的可能原因，以大大加快云中异常故障排除过程。该公司将实施软件产品，并与合作伙伴就现实世界的云计算基础设施进行案例研究。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to greatly improve the robustness and diagnosability of many real world cloud computing infrastructures. The proposed technology will significantly reduce the downtime of production cloud systems, which can attract more users to adopt cloud computing technology and thus benefit the expanding segment of society and the economy that depends on cloud technology. The project will also advance the state of the art of cloud system reliability research by putting research results into real world use.This Small Business Innovation Research (SBIR) Phase I project will transform system anomaly management for production cloud computing infrastructures. The novelty of the company's solution lies in three unique features: 1) it provides automatic multivariate anomaly detection that can enable high-fidelity anomaly alerts without imposing any configuration burden on the user; 2) it provides early anomaly alerts before big system problems occur; and 3) it provides anomaly diagnosis that can generate hints on why an anomaly occurs. The proposed research will produce novel and practical anomaly prediction and diagnosis solutions that will be validated in real world cloud infrastructures. Specifically, the project consists of two thrusts: 1) online multivariate anomaly prediction that explores new light-weight unsupervised learning algorithms for achieving high-fidelity anomaly alerts and providing time-to-failure estimations; and 2) automatic anomaly diagnosis that can identify possible causes of an anomaly to greatly expedite the anomaly troubleshooting process in the cloud. The company will implement the software products and carry out case studies with partners on real world cloud computing infrastructures.