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

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.