β‘ NetPredict: High-Speed GBDT Latency Forecaster with Conformal Coverage
Model Description
NetPredict is an optimized LightGBM Gradient Boosted Decision Tree (GBDT) regressor designed for microsecond-scale point latency forecasting in high-concurrency cloud networks and distributed telemetry pipelines.
When paired with Split Conformal Prediction, the model provides distribution-free, finite-sample coverage guarantees ($1 - \alpha = 0.95$) over non-linear queuing delays and bufferbloat anomalies.
- Author: Ankit Kumar Patel (The-AnkitPatel)
- Affiliation: National Institute of Technology, Uttarakhand | IIT Roorkee Telemetry Testbed
- Open Source Role: Upstream Contributor @ Cloud Native Computing Foundation (CNCF OpenTelemetry)
- Framework: LightGBM GBDT + Scipy Conformal Calibration
- Inference Latency: $0.14\text{ms}$ ($p99$ SLA)
Intended Use & Capabilities
- eBPF-driven flow scheduling: Predict buffer saturation 5β15 minutes before tail-drop packet loss.
- Uncertainty-aware microservices: Yields exact $[\hat{y} - q, \hat{y} + q]$ intervals with guaranteed $95.0%$ empirical test set coverage.
Performance & Evaluation Metrics
| Metric | Holdout Test Value | Unit |
|---|---|---|
| RMSE | 0.3982 |
milliseconds |
| MAE | 0.2814 |
milliseconds |
| $R^2$ Score | 0.9924 |
dimensionless |
| 95% Conformal Coverage | 95.12% |
empirical guarantee satisfied |
| $p99$ Inference Latency | 0.142 |
milliseconds |
Quickstart & Inference
import pandas as pd
import lightgbm as lgb
from huggingface_hub import hf_hub_download
# Download model weights from Hugging Face Hub
model_path = hf_hub_download(repo_id="The-AnkitPatel/netpredict-telemetry-lgbm", filename="model.txt")
bst = lgb.Booster(model_file=model_path)
# Example telemetry ingress vector
sample_flow = pd.DataFrame([{
'ingress_mbps': 650.0,
'active_flows': 220,
'arrival_kpps': 580.0,
'buffer_util_pct': 74.5,
'loss_rate_pct': 0.4,
'jitter_ms': 3.2
}])
latency_point_pred = bst.predict(sample_flow)
print(f"Predicted One-Way Queuing Latency: {latency_point_pred[0]:.3f} ms")
Live Interactive Space
Test the model live with interactive sliders at:
π Hugging Face Space: NetPredict Conformal Telemetry
Evaluation results
- Root Mean Squared Error (ms)self-reported0.398
- Mean Absolute Error (ms)self-reported0.281
- R-squaredself-reported0.992