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RCInvestigator: Towards Better Investigation of Anomaly Root Causes in Cloud Computing Systems
Summary
Root cause analysis (RCA) in cloud systems is challenging. RCInvestigator enhances RCA by integrating human expertise with machine analysis for efficient and reliable system investigations.
Area of Science:
- Computer Science
- Data Science
- Human-Computer Interaction
Background:
- Cloud computing systems generate large-scale, high-dimensional monitoring data, making root cause analysis (RCA) difficult.
- Existing RCA methods are either manual and time-consuming or automated but lack domain expert insights.
- Ensuring the availability and efficiency of cloud systems necessitates effective RCA.
Purpose of the Study:
- To propose RCInvestigator, a visual analytics system for interactive root cause investigation.
- To bridge the gap between manual and automated RCA by fostering human-machine collaboration.
- To address challenges in modeling databases, inferring causes from time-series data, and presenting comprehensible results.
Main Methods:
- Developed RCInvestigator, a visual analytics system for collaborative root cause investigation.
- Implemented methods for modeling databases relevant to RCA.
- Designed algorithms for inferring root causes from large-scale time-series data.
- Focused on generating comprehensible investigation results for domain experts.
Main Results:
- Demonstrated the effectiveness and utility of RCInvestigator through two real-world case studies.
- Showcased successful integration of human expertise and machine analysis in RCA.
- Validated the system's ability to handle large-scale, high-dimensional monitoring data.
Conclusions:
- RCInvestigator facilitates interactive root cause investigation by enabling tight collaboration between human experts and machine analysis.
- The system effectively addresses key challenges in cloud system RCA, improving flexibility and reliability.
- Real-world case studies confirmed the positive feedback and utility of RCInvestigator from domain experts.
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