This software implements the anomaly detection algorithms proposed in paper https://ieeexplore.ieee.org/abstract/document/9006046. To reproduce the results, you need to go to ANL ALCF website to download MIRA data first https://reports.alcf.anl.gov/data/mira.html.

• gcc compiler that supports C99.
• python3
• pytorch 1.0
• R

I suggest users to install these packages with anaconda.

Use make file to compile code and test cases with command "make".

Unzip all RAS_EVENT data you have downloaded. Unzip the dataset and put in the same folder.

• Use the R code in https://github.com/QiaoK/AMASE/blob/master/developer_note.txt to clean up the data.
• Compile the C++ code. Use the "./streaming_features" to generate the training and testing features.
• Usage: ./streaming_feature warn_table_name fatal_table_name fatal_t_threshold window_size lead_time_count
• Example: ./streaming_feature sys_events.csv sys_fatal_spatial.csv 1 300 4
• Run the python training code for neural network training.
• Usage: python training_lead_time.py feature_file_name training_epochs retrain(0 or 1) reload_data(0 or 1) lead_time_interval(3600) lead_time_stride(1)
• feature_file_name is the output file generated by ./streaming_feature

Contact me at [email protected] if you have questions.