ML Anthology

CCA: An ML Pipeline for Cloud Anomaly Troubleshooting

Lili Georgieva, Ioana Giurgiu, Serge Monney, Haris Pozidis, Viviane Potocnik, Mitch Gusat
AAAI 2022 pp. 13167-13169
doi:10.1609/AAAI.V36I11.21716 /aaai/2022/georgieva2022aaai-cca/

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?

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Cite

Text

Georgieva et al. "CCA: An ML Pipeline for Cloud Anomaly Troubleshooting." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21716

Markdown

[Georgieva et al. "CCA: An ML Pipeline for Cloud Anomaly Troubleshooting." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/georgieva2022aaai-cca/) doi:10.1609/AAAI.V36I11.21716

BibTeX

@inproceedings{georgieva2022aaai-cca,
  title     = {{CCA: An ML Pipeline for Cloud Anomaly Troubleshooting}},
  author    = {Georgieva, Lili and Giurgiu, Ioana and Monney, Serge and Pozidis, Haris and Potocnik, Viviane and Gusat, Mitch},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13167-13169},
  doi       = {10.1609/AAAI.V36I11.21716},
  url       = {https://mlanthology.org/aaai/2022/georgieva2022aaai-cca/}
}