Name
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? |
Year | Venue | Keywords |
|---|---|---|
2022 | AAAI Conference on Artificial Intelligence | Causality,Explainability,Timeseries,Cloud |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lili Georgieva | 1 | 0 | 0.34 |
| Ioana Giurgiu | 2 | 213 | 14.09 |
| Serge Monney | 3 | 0 | 0.34 |
| Haris Pozidis | 4 | 2 | 1.04 |
| Viviane Potocnik | 5 | 0 | 0.34 |
| Mitch Gusat | 6 | 0 | 0.34 |