CCA: An ML Pipeline for Cloud Anomaly Troubleshooting

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

[AAAI-22] Demonstrations

Keywords
Poster Session 4 @ Blue 1, Poster Session 11 @ Blue 1, Poster Session 4, Poster Session 11

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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?

Sessions where this paper appears

  • Poster Session 4

    Blue 1

  • Poster Session 11

    Blue 1