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Source code for GTrace (ESEC/FSE'23 industry track).

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# GTrace
Code and dataset for [(ESEC/FSE' 23) From Point-wise to Group-wise: A Fast and Accurate
Microservice Trace Anomaly Detection Approach](https://netman.aiops.org/wp-content/uploads/2023/11/GTrace_FSE_Industry2023_upload.pdf).

## Evaluation of Accuracy
We provide dataset B for evaluation.
- Download [dataset_b.zip](https://cloud.tsinghua.edu.cn/f/d7868566fb344541bb26/?dl=1) and put it under `dataset/` after unzipping it.
- Install Python 3.8+ on your system.
- Run `pip3 install -r requirements.txt` to install the dependencies.
- Run `python3 -m tracegnn.models.gtrace.main` to start training. The evaluation will automatically starts after training.
- If you want to run on GPU, you can modify the `device` in `tracegnn/models/config.py`.
  
(To enable accuracy evaluation, the model runs the inference once for each trace and thus cannot be used for time efficiency evaluation. If you need to perform evaluation of time efficiency, please refer to the following section.)

## Evaluation of Time Efficiency
We provide the code for the `Anomaly Detection` module and `Graph Building` module.
__The code has not been tested on the Windows platform; please use a Mac or Linux system.__

To evaluate the time efficiency, we provide a minimal example and a trained model that can be run directly on your local device without deployment:
- Run `cd deployment`.
- Install `GCC 9.3.0+`, `make` and `CMake 3.2+` on your device. Run `bash build.sh` to download and build the dependencies.
- Run `sh run_local.sh` to evaluate the time efficiency.
- Install `Intel SVML` to get better performance on Intel CPU. (See https://numba.readthedocs.io/en/stable/user/performance-tips.html#intel-svml).
- __When the the program is complete, it will enter a waiting state and will not exit automatically. Therefore, you need to manually kill it.__

## Visualization Tool
- Run `python3 -m tracegnn.visualization.webviewer_server`.
- Visit `http://localhost:12312/0` or `http://localhost:12312/1` to see the visualization results for two example cases.

# Reference
- LRUCache11: https://github.com/mohaps/lrucache11.git
- Kubernetes: https://kubernetes.io/
- PyTorch: https://pytorch.org/
- DGL: https://dgl.ai/
- CMake: https://cmake.org/

# Notes
- Dataset A is under review by the company and cannot be disclosed at this time.

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