Objective
Build a regression model that can predict the latency (in milliseconds) of a network path based on characteristics like distance, time of day, and current traffic load to enable proactive network performance management.
Business Value
- Proactive Monitoring: Predict network performance issues before they impact users
- Capacity Planning: Make data-driven decisions about network infrastructure investments
- SLA Management: Ensure service level agreements are met through predictive analytics
- Route Optimization: Select optimal network paths based on predicted performance
- Real-Time Decision Making: Enable automatic failover and traffic rerouting based on latency predictions
Core Libraries
- pandas & numpy: Data manipulation and numerical operations
- xgboost: Gradient boosting framework for high-performance regression
- scikit-learn: Model evaluation metrics and data splitting utilities
- matplotlib & seaborn: Data visualization and performance analysis
Dataset
Source: Synthetically Generated Network Monitoring Data- Size: 5,000 network path measurements
- Features: Distance, hour of day, congestion factors, traffic spikes
- Target: Network latency in milliseconds
- Scope: Realistic simulation of WAN performance characteristics
- Quality: Physics-based modeling with appropriate noise and variability
Step-by-Step Guide
1. Environment Setup
# Install required packages
pip install pandas numpy xgboost scikit-learn matplotlib seaborn
# All data is synthetically generated - no external APIs needed
2. Synthetic Data Generation
import pandas as pd
import numpy as np
# Generate realistic network latency dataset
def generate_latency_data(num_samples=5000):
data = []
for _ in range(num_samples):
distance_km = np.random.randint(50, 5000)
hour_of_day = np.random.randint(0, 24)
# Business hours congestion (9am to 5pm)
if 9 <= hour_of_day <= 17:
congestion_factor = np.random.uniform(1.2, 2.5)
else:
congestion_factor = np.random.uniform(0.8, 1.2)
# Random traffic spikes
traffic_spike = np.random.choice([0, 1], p=[0.9, 0.1]) * np.random.uniform(5, 15)
# Physics-based latency calculation
base_latency = 5 # Local processing time
distance_latency = distance_km * 0.05 # Speed of light effect
congestion_latency = hour_of_day * congestion_factor
random_noise = np.random.normal(0, 5) # Jitter simulation
latency = base_latency + distance_latency + congestion_latency + traffic_spike + random_noise
latency = max(5, latency) # Ensure realistic minimum
data.append([distance_km, hour_of_day, congestion_factor, traffic_spike, latency])
return pd.DataFrame(data, columns=['distance_km', 'hour_of_day', 'congestion_factor', 'traffic_spike', 'latency_ms'])
3. Exploratory Data Analysis
import seaborn as sns
import matplotlib.pyplot as plt
# Visualize feature relationships
sns.pairplot(df, x_vars=['distance_km', 'hour_of_day', 'congestion_factor'],
y_vars=['latency_ms'], height=4, aspect=1)
plt.suptitle('Latency vs. Key Features')
plt.show()
# Correlation analysis
correlation_matrix = df.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)
plt.title('Feature Correlation Matrix')
plt.show()
4. Data Preparation and Splitting
from sklearn.model_selection import train_test_split
# Prepare features and target
feature_cols = ['distance_km', 'hour_of_day', 'congestion_factor', 'traffic_spike']
X = df[feature_cols]
y = df['latency_ms']
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
5. Model Training with XGBoost
import xgboost as xgb
# Configure XGBoost Regressor
model = xgb.XGBRegressor(
objective='reg:squarederror', # Regression objective
n_estimators=100, # Number of boosting rounds
learning_rate=0.1, # Step size shrinkage
max_depth=6, # Maximum tree depth
random_state=42,
n_jobs=-1 # Use all CPU cores
)
# Train the model
model.fit(X_train, y_train)
6. Model Evaluation
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
# Make predictions
y_pred = model.predict(X_test)
# Calculate regression metrics
mae = mean_absolute_error(y_test, y_pred)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
print(f"Mean Absolute Error (MAE): {mae:.2f} ms")
print(f"Root Mean Squared Error (RMSE): {rmse:.2f} ms")
print(f"R-squared (R²): {r2:.2%}")
7. Visualization and Analysis
# Actual vs. Predicted scatter plot
plt.figure(figsize=(8, 8))
plt.scatter(y_test, y_pred, alpha=0.5)
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()],
'--r', linewidth=2, label='Perfect Prediction')
plt.xlabel('Actual Latency (ms)')
plt.ylabel('Predicted Latency (ms)')
plt.title('Model Performance: Actual vs. Predicted')
plt.legend()
plt.show()
# Feature importance analysis
xgb.plot_importance(model, height=0.8)
plt.title('Feature Importance in Latency Prediction')
plt.show()
8. Real-Time Prediction Function
def predict_network_latency(distance_km, hour_of_day, congestion_factor, traffic_spike):
"""
Predict network latency for given conditions
Args:
distance_km: Physical distance of network path
hour_of_day: Current hour (0-23)
congestion_factor: Network congestion multiplier
traffic_spike: Presence of traffic spike (0 or spike value)
Returns:
Predicted latency in milliseconds
"""
features = np.array([[distance_km, hour_of_day, congestion_factor, traffic_spike]])
prediction = model.predict(features)[0]
return round(prediction, 2)
# Example usage
predicted_latency = predict_network_latency(
distance_km=1500,
hour_of_day=14, # 2 PM
congestion_factor=1.8,
traffic_spike=0
)
print(f"Predicted latency: {predicted_latency} ms")
Success Criteria
- Primary Metric: R-squared > 0.85 for variance explanation
- Accuracy: Mean Absolute Error < 10ms for practical network management
- Robustness: Model handles various network conditions and distances
- Speed: Real-time prediction capability for operational use
- Interpretability: Clear feature importance for network engineering insights
Next Steps & Extensions
Technical Enhancements
1. Time Series Analysis: Add temporal patterns and seasonal effects
2. Advanced Features: Include network topology, link utilization, and protocol overhead
3. Ensemble Methods: Combine XGBoost with other algorithms for improved accuracy
4. Real-Time Learning: Implement online learning for continuous model adaptation
Business Applications
1. NOC Dashboard: Real-time latency prediction for network operations centers
2. Traffic Engineering: Optimize routing decisions based on predicted performance
3. SLA Monitoring: Proactive alerts before performance degrades below thresholds
4. Capacity Planning: Predict when network upgrades will be needed
Advanced Analytics
1. Confidence Intervals: Provide prediction uncertainty for better decision making
2. Anomaly Detection: Identify unusual latency patterns that may indicate issues
3. What-If Analysis: Simulate different network scenarios for planning purposes
4. Multi-Path Optimization: Extend to predict performance of alternative routes
Files in this Project
- README.md - Project documentation and implementation guide
- latency_jitter_prediction.ipynb - Complete Jupyter notebook implementation
- requirements.txt - Python package dependencies
Key Insights
- Distance is the dominant factor in network latency prediction due to speed of light limitations
- Business hours significantly impact network performance through congestion patterns
- XGBoost effectively captures non-linear relationships between network variables
- Feature importance analysis provides actionable insights for network engineering decisions
- The model enables "what-if" analysis for network planning and optimization scenarios
Physics-Based Modeling
- Base Latency: Local processing and switching delays
- Distance Effect: Speed of light propagation delays (≈5ms per 1000km)
- Congestion Modeling: Business hours traffic patterns with realistic multipliers
- Traffic Spikes: Random events that simulate network anomalies
- Jitter Simulation: Gaussian noise to represent natural network variability