This project realizes the anomaly detection and root cause location of multimodal data. The anomaly detection part
adopts MTAD-GAT model (metric, trace) and DeepLog model (log), The root cause localization part adopts the Squeeze model.
- Train:
- Train data (update model): 2022-03-24 15:20:00 ~ 2022-03-25 08:06:00
- Valid data (prevent overfitting): 2022-03-25 08:07:00 ~ 2022-03-25 15:19:00
- Test:
- Valid data (search threshold): 2022-03-26 08:30:00 ~ 2022-03-26 11:29:00
- Test data (evaluation model): 2022-03-26 11:30:00 ~ 2022-03-26 20:29:00
|
|
P |
R |
F1 |
| metric |
- |
0.5329 |
0.7945 |
0.6379 |
| metric |
+ |
0.8873 |
0.7412 |
0.8077 |
| trace |
- |
0.1943 |
0.3527 |
0.2506 |
| trace |
+ |
0.2073 |
0.8706 |
0.3348 |
| log |
- |
0.1382 |
0.4027 |
0.2058 |
| log |
+ |
0.1759 |
1.0000 |
0.2992 |
| metric+trace |
- |
0.3190 |
0.6218 |
0.4217 |
| metric+trace |
+ |
0.7917 |
0.8941 |
0.8398 |
| metric+trace+log |
- |
0.3347 |
0.6359 |
0.4386 |
| metric+trace+log |
+ |
0.8085 |
0.8941 |
0.8492 |
|
|
PR@1 |
PR@2 |
PR@3 |
PR@4 |
PR@5 |
PR@Avg |
| RootCause |
- |
0.2783 |
0.4001 |
0.5192 |
0.5953 |
0.6217 |
0.4829 |
| RootCause |
+ |
0.5739 |
0.7652 |
0.8522 |
0.9217 |
0.9391 |
0.8104 |
- See Log for training and testing logs.
- See Loss for loss visualization.