The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will be to greatly improve the robustness and diagnosability of many computing infrastructures including both public and private computing clouds. The proposed technology will significantly reduce the occurrence of performance degradation and service downtime in cloud computing infrastructures, which can attract more users to adopt cloud computing technology and thus benefit society as a whole, which depends increasingly on cloud technology. The project will also advance the state of the art in cloud system reliability research by putting research results into real world use.
This Small Business Innovation Research (SBIR) Phase II project will transform system anomaly management for dynamic complex computing infrastructures. The novelty of the company's solution lies in three unique features: 1) predictive: the solution can raise advance alerts before a serious service outage occurs; 2) self-learning: the solution automatically infers alert conditions and performs automatic root cause analysis using machine learning algorithms; 3) adaptive: the technology adapts to dynamic systems. The proposed research will produce novel and practical anomaly prediction and diagnosis solutions that will be validated in real world computing infrastructures. Specifically, the project consists of three thrusts: 1) adaptive learning in dynamic environments; 2) real-time feature extraction and pattern recognition over system metric and log data; and 3) full stack root cause analysis. During the project the company will implement its software products and carry out case studies with prospective customers on real world computing infrastructures.
