Practical Root Cause Localization for Microservice Systems via Trace Analysis. IWQoS 2021

The study data is public at

• OneDrive: https://1drv.ms/u/s!Ao2DxaN2zku_bAUszKmCUiodw94?e=7ThI47
• Tsinghua Cloud https://cloud.tsinghua.edu.cn/d/8371855eddd64a8db23b/ (**大陆可访问)

The experiment workflow is controlled via the Makefile. The input and output of each step can be referred to the Makefile

• run_selecting_features.py: Feature selection
• run_anomaly_detection_invo.py: Anomaly detection based on the useful features
• run_localization_association_rule_mining_20210516.py: Root-cause service ocalization
• prepare_train_file_tmp.py is used to split the dataset into train and test datasets. Note that this step is not included in the Makefile.

Presentation Video

If the dataset is helpful, please cite the paper.

@inproceedings{li2021practical,
  title={Practical Root Cause Localization for Microservice Systems via Trace Analysis},
  author={Li, Zeyan and Chen, Junjie and Jiao, Rui and Zhao, Nengwen and Wang, Zhijun and Zhang, Shuwei and Wu, Yanjun and Jiang, Long and Yan, Leiqin and Wang, Zikai and others},
  booktitle={IEEE/ACM International Symposium on Quality of Service (IWQoS) 2021},
  year={2021},
  publisher = {{IEEE}}
}