GIED

This repo contains the simulation environment dataset and source code showpieces for the paper "Graph based Incident Extraction and Diagnosis in Large-Scale Online Systems" (ASE'22).

Project Structure

• ./data contains the simulation environment dataset used in the paper. The real-world dataset from the company cannot be provided here yet due to the confidentiality policy of the company.
• ./showpieces contains ipython notebooks which run some code pieces of GIED to show how each step is performed. Their order is as follow:
  • anomaly_detection_and_issue_extraction.ipynb contains code pieces for KPI anomaly detection and issue extraction.
  • data_labelling.ipynb contains code pieces for data labelling using fault injection records.
  • feature_engineering.ipynb contains code pieces for feature engineering.
  • SpatioDevNetPackage contains the implemented graph neural networks based model.
  • incident_detection.ipynb contains code pieces for the graph neural networks based model training and testing for incident detection.
  • incident_diagnosis.ipynb contains code pieces for the root cause service localization.

Citation

If you find this work useful, please cite our paper:

@inproceedings{DBLP:conf/kbse/HeCLYCYL22,
  author       = {Zilong He and
                  Pengfei Chen and
                  Yu Luo and
                  Qiuyu Yan and
                  Hongyang Chen and
                  Guangba Yu and
                  Fangyuan Li},
  title        = {Graph based Incident Extraction and Diagnosis in Large-Scale Online
                  Systems},
  booktitle    = {37th {IEEE/ACM} International Conference on Automated Software Engineering,
                  {ASE} 2022, Rochester, MI, USA, October 10-14, 2022},
  pages        = {48:1--48:13},
  publisher    = {{ACM}},
  year         = {2022},
  url          = {https://doi.org/10.1145/3551349.3556904},
  doi          = {10.1145/3551349.3556904},
  timestamp    = {Thu, 22 Jun 2023 07:45:51 +0200},
  biburl       = {https://dblp.org/rec/conf/kbse/HeCLYCYL22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}