PSqueeze

Implementation and dataset for Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems (Accepted by Journal of Systems of Software), which extends our previous conference version

Preprint: https://arxiv.org/abs/2305.03331

Paper: https://www.sciencedirect.com/science/article/abs/pii/S0164121223001437

Citation

@article{li2023generic,
  title = {Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems},
  author = {Li, Zeyan and Chen, Junjie and Chen, Yihao and Luo, Chengyang and Zhao, Yiwei and Sun, Yongqian and Sui, Kaixin and Wang, Xiping and Liu, Dapeng and Jin, Xing and Wang, Qi and Pei, Dan},
  year = {2023},
  journal = {Journal of Systems and Software},
  pages = {111748},
  doi = {10.1016/j.jss.2023.111748},
  keywords = {Multi-dimensional data,Online service system,Ripple effect,Root cause localization}
}

Requirements

At least python>=3.6 is required.

pip install -r requirements.txt

A virtual environment is strongly recommended.

Datasets

All datasets are available at Zenodo

For simulation datasets, the ground-truth root causes are in injection_info.csv in each subfolder.

For injection datasets, each subdirectory contains monitoring data for one fault injection. Their ground-truth root causes are indicated by the subdirectory names.

Usage

Simulation Datasets

For convenience, run_exp.sh provide a script to run all experiments.

Alternatively, you can run each experiment by yourself.

python run_algorithm.py --help

Usage: run_algorithm.py [OPTIONS]

  :param name: :param input_path: :param output_path: :param num_workers:
  :param kwargs: :return:

Options:
  --name TEXT            name of this setting
  --input-path TEXT      will read data from {input_path}/{name}
  --output-path TEXT     if {output_path} is a dir, save to
                         {output_path}/{name}.json; otherwise save to
                         {output_path}
  --num-workers INTEGER  num of processes
  --derived              means we should read {timestamp}.a.csv and
                         {timestamp}.b.csv
  --help                 Show this message and exit.

python run_evaluation.py --help

Usage: run_evaluation.py [OPTIONS]

Options:
  -i, --injection-info TEXT  injection_info.csv file
  -p, --predict TEXT         output json file
  -c, --config TEXT          config json file
  -o, --output-path TEXT     output path
  --help                     Show this message and exit.

The config json file should contain the attribute names, e.g.:

{
  "columns": [
    "a", "b", "c", "d"
  ]
}

Injection Datasets

Run this command: python3 run_algorithms_on_train_ticket_experiment.py [path_to_injection_data] For example, python3 run_algorithms_on_train_ticket_experiment.py istio-food-end_station-delay-at-2022-10-09-23-00