GTrace

Code and dataset for GTrace.

Evaluation of Accuracy

We provide dataset B for evaluation. The dataset is under the dataset folder.

• 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.

Evaluation of Time Efficiency

We provide the code for the Anomaly Detection module and Graph Building module.

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).

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/