DATA SCIENCE & SYSTEM SECURITY
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IT operation is one of the technological foundations of the increasingly digitalized world. It is responsible for ensuring that digitalized businesses and societies run reliably, efficiently and safely. With the rapid advances in networking, computers, and hardware, we face an explosive growth of complexity in networked applications and information services. These large-scale, often distributed, information systems usually consist of a great variety of components that work together in a highly complex, coordinated, and evolving manner. Moreover, terabytes of heterogeneous data per day including metrics data, log data and event data, overwhelm Ops engineers.
This project aims to develop unique AI techniques that provide a 360-degree understanding of IT operations and fully automate critical dimensions of IT operations monitoring, including historical analysis, anomaly detection, root cause localization, event forecasting, and performance analysis. Our solution for AIOps is a continuous automatic IT operations management system powered by deep neural networks, graph AI, and statistical models.
Besides application in AIOps, our techniques can offer “Intelligent operation and maintenance” as a service for a wide range of applications, including industrial preventive maintenance, healthcare, cyber security, automobile, and smart factories.
Team Members: Zhengzhang Chen, Junxiang Wang, Haifeng Chen
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems. Previous data-driven RCA methods, particularly those employing causal discovery techniques, have primarily focused on constructing dependency
The task of root cause analysis (RCA) is to identify the root causes of system faults/failures by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure recovery and mitigate system damages or financial losses. However, previous research has mostly focused on developing
The goal of root cause analysis is to identify the underlying causes of system problems by discovering and analyzing the causal structure from system monitoring data. It is indispensable for maintaining the stability and robustness of large-scale complex systems. Existing methods mainly focus on the
