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bertops/README.md

BERTOps: Learning Representations on Logs for AIOps

Dataset Distribution

Note: Percentage split of public vs proprietary dataset is about 0.937 % in both training and validation dataset. Below are the dataset distribution for each log source used for training BERTOps.

Proprietary Dataset

Log Source #Train #Validation #Total
Haproxy 5000 1250 6250
MongoDB 120,000 30,000 150,000
Apache 165,185 41,297 206,482
Sockshop 86,972 21,743 108,715
Robotshop 17,280 4320 21,600
TOTAL 394,437 98,610 493,047

Public Dataset

Link to dataset: https://zenodo.org/record/3227177#.Y5dpGi8RqJ9

Log Source #Train #Validation #Total
Proxifier 17,063 4266 21,329
Linux 34,542 8636 43,178
Zookeeper 59,504 14,876 74,380
Thunderbird 80,000 20,000 100,000
Mac 93,826 23,457 117,283
OpenStack 166,255 41,564 207,819
HPC 346,792 86,698 433,490
Android 1,244,004 311,001 1,555,005
BGL 3,798,370 949,593 4,747,963
HDFS 8,940,503 2,235,126 11,175,629
Spark 26,902,298 6,725,575 33,627,873
TOTAL 41,683,157 10,420,792 52,103,949

Annotated Datasets

Annotated Datasets have been pushed in the data\ folder.

Results

The submitted version of our paper illustrates results in terms of accuracy. We present here the results in terms of precision, recall and f1-score. Please check the results/ folder.

Fault Category Prediction Results: results/fc.png Golden Signal Classification Results: results/gs.png Log Format Recognition Results: results/lfd.png

Reproducing the results

We have also pushed here, our training scripts with the appropriate hyper-parameters to prepare a pretrained model for ITOps domain.

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