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2022 AAAI AAAI 2022

CCA: An ML Pipeline for Cloud Anomaly Troubleshooting

Abstract

Abstract Cloud Causality Analyzer (CCA) is an ML-based analytical pipeline to automate the tedious process of Root Cause Analysis (RCA) of Cloud IT events. The 3-stage pipeline is composed of 9 functional modules, including dimensionality reduction (feature engineering, selection and compression), embedded anomaly detection, and an ensemble of 3 custom explainability and causality models for Cloud Key Performance Indicators (KPI). Our challenge is: How to apply a reduced (sub)set of judiciously selected KPIs to detect Cloud performance anomalies, and their respective root causal culprits, all without compromising accuracy?

🧭 Keyword Pioneer — cloud anomaly detection
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Lili Georgieva , Ioana Giurgiu , Serge Monney , Haris Pozidis , Viviane Potocnik , Mitch Gusat

Topics

Machine Learning > Application Areas > Efficient Computing Machine Learning > Learning Types > Causal Inference Machine Learning > Core Methods > Anomaly Detection

Keywords

causal inference dimensionality reduction anomaly detection ensemble learning ensemble method cloud computing root cause analysis cloud anomaly detection causality model
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