fse-baro 0.1.2

BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection

🕵️ BARO: Root Cause Analysis for Microservices

BARO is an end-to-end anomaly detection and root cause analysis approach for microservices's failures. This repository includes artifacts for reuse and reproduction of experimental results presented in our FSE'24 paper titled "BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection".

Installation

Install from PyPI

pip install fse-baro

Or, build from source

git clone https://github.com/phamquiluan/baro.git && cd baro
pip install -e .

BARO has been tested on Linux and Windows, with different Python versions. More details are in INSTALL.md.

How-to-use

import pandas as pd 
from baro.anomaly_detection import bocpd
from baro.root_cause_analysis import robust_scorer
from baro.utility import download_data, read_csv

# download a sample data to data.csv
download_data()

# read data, perform anomaly detection and rca using bocpd and robust_scorer
data = read_csv("data.csv")
anomalies = bocpd(data)  # data format and visualization are described in the Colab notebook above.
root_causes = robust_scorer(data, anomalies=anomalies)
print(root_causes)

# reproducibility
TODO: add reproducibility

Download Paper

TBD

Download Datasets

Our datasets are publicly available in Zenodo repository with the following information:

• Dataset DOI:
• Dataset URL: https://zenodo.org/records/11046533

Reproducibility

To reproduce the performance of our BARO, we have prepared two Google Colab Notebooks as follows,

1. : This notebook reproduces the RCA performance of BARO (also at tutorials/reproducibility.ipynb).
2. : This nodebook reproduces the output of the Multivariate BOCPD module.

Running Time & Instrumentation Cost

Please refer to our docs/running_time_and_instrumentation_cost.md document.

Citation

@inproceedings{pham2024baro,
  title={BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection},
  author={Luan Pham, Huong Ha, and Hongyu Zhang},
  booktitle={Proceedings of the ACM on Software Engineering, Vol 1},
  year={2024},
  organization={ACM}
}

Contact

[e-mail]