RCInvestigator: Towards Better Investigation of Anomaly Root Causes in Cloud Computing Systems
S.M Liu, Yunfan Zhou, Jue Zhang, Shandan Zhou, Weiwei Cui, Qingwei Lin, Thomas Moscibroda, Haidong Zhang, Di Weng, Yingcai Wu · IEEE Transactions on Visualization and Computer Graphics · 2026
Root cause analysis (RCA) is critical for maintaining the availability and efficiency of cloud computing systems. However, identifying root causes from the large-scale, high-dimensional monitoring data generated by these complex environments is a significant challenge. Current approaches often rely on time-consuming manual analysis to ensure flexibility and reliability, while recent automated methods lack the crucial insights provided by domain experts. To bridge this gap, we propose RCInvestigator, a visual analytics system that facilitates interactive root cause investigation by establishing a tight collaboration between human experts and machine analysis. Our approach addresses three key challenges: a) modeling databases for the root cause investigation, b) inferring root causes from large-scale time series, and c) building comprehensible investigation results. We demonstrate the effectiveness and utility of RCInvestigator through two real-world case studies, which received positive feedback from domain experts.