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

SBIR 第一阶段：为云基础设施提供自动异常预测和诊断软件即服务

• 批准号: 1548867
• 负责人: Chao Huang
• 金额: $ 15万
• 依托单位: InsightFinder Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548867&HistoricalAwards=false
• 关键词: SBIR; Phase; Providing; Automatic; Anomaly

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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将大大提高许多真实的世界云计算基础设施的鲁棒性和可诊断性。该技术将大大减少生产云系统的停机时间，从而吸引更多用户采用云计算技术，从而使依赖云技术的社会和经济部门受益。该项目还将通过将研究成果投入真实的世界使用，推进云系统可靠性研究的最新水平。这一小型企业创新研究（SBIR）第一阶段项目将为生产云计算基础设施改造系统异常管理。该公司的解决方案的新奇在于三个独特的功能：1）它提供了自动多变量异常检测，可以在不给用户带来任何配置负担的情况下启用高保真异常警报; 2）它在大的系统问题发生之前提供早期异常警报; 3）它提供了异常诊断，可以生成关于异常发生原因的提示。拟议的研究将产生新颖实用的异常预测和诊断解决方案，这些解决方案将在真实的世界云基础设施中得到验证。具体来说，该项目包括两个重点：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，大大加快云中的异常故障排除过程。该公司将实施软件产品，并与合作伙伴一起对真实的世界云计算基础设施进行案例研究。