Model energy and latency metrics
| Model | Training time (sec) | Train CO2 (kg) | Inference latency (msec) | Inference CO2 (kg) |
|---|---|---|---|---|
| Deep AE + LightGBM | 206.00 | 0.00235 | 0.040 | 2.17e−06 |
| LSTM | 172.69 | 0.00137 | 0.025 | 1.23e−06 |
| Rule-Based | 1.44 | 6.77e−08 | 0.260 | N/A |
| TranAD | 4172.00 | 0.017364 | 0.4802 | 2.00e−05 |
| LogBERT | 6680.00 | 0.027808 | 25.5135 | 0.001048 |
| Edge-Aware Transformer GNN (HeteroConv + TransformerConv) | 36.00 | 0.000141 | 0.0094 | 2.00e−06 |
| Graph Autoencoder (GAE) | 11.00 | 0.000043 | 0.0001 | 3.25e−08 |
| Variational Graph Autoencoder (VGAE) | 10.00 | 0.000037 | 0.0001 | 3.22e−08 |
| Meta-Classifier | 198.98 | 0.00124 | 0.028 | 4.03e−07 |
| Model | Training time (sec) | Train CO2 (kg) | Inference latency (msec) | Inference CO2 (kg) |
|---|---|---|---|---|
| Deep AE + LightGBM | 206.00 | 0.00235 | 0.040 | 2.17e−06 |
| LSTM | 172.69 | 0.00137 | 0.025 | 1.23e−06 |
| Rule-Based | 1.44 | 6.77e−08 | 0.260 | N/A |
| TranAD | 4172.00 | 0.017364 | 0.4802 | 2.00e−05 |
| LogBERT | 6680.00 | 0.027808 | 25.5135 | 0.001048 |
| Edge-Aware Transformer GNN (HeteroConv + TransformerConv) | 36.00 | 0.000141 | 0.0094 | 2.00e−06 |
| Graph Autoencoder (GAE) | 11.00 | 0.000043 | 0.0001 | 3.25e−08 |
| Variational Graph Autoencoder (VGAE) | 10.00 | 0.000037 | 0.0001 | 3.22e−08 |
| Meta-Classifier | 198.98 | 0.00124 | 0.028 | 4.03e−07 |
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