Documentation | Augment AI Platform

Installation

Deploy AI NetworkOptimizer to start intelligent network routing optimization with machine learning.

System Requirements

Linux kernel 4.9+ with eBPF support
Python 3.8+ and Node.js 16+ for AI models
Network admin privileges for routing table access
Minimum 2GB RAM (4GB recommended for large networks)
Access to network infrastructure and routing devices

Install via Package Manager

Installationbash

# Install via pip
pip install augment-network-optimizer

# Install via npm for monitoring dashboard
npm install -g @augment/network-optimizer-cli

# Install from source
git clone https://github.com/augment-ai/network-optimizer
cd network-optimizer
pip install -e .

# Install network dependencies
sudo apt-get install iproute2 iptables tcpdump

# Verify installation
net-optimizer --version

Network Infrastructure Integration

Configure integration with your network infrastructure and monitoring systems:

Network Integrationbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure network device access
export NETWORK_DEVICES="router1.company.com,switch1.company.com"
export SNMP_COMMUNITY=public
export SSH_KEY_PATH=/path/to/network-key

# Configure monitoring integration
export PROMETHEUS_URL=http://prometheus:9090
export INFLUXDB_URL=http://influxdb:8086

# Initialize network optimizer
net-optimizer init --discover-topology

# Verify network access
net-optimizer health-check --test-connectivity

Quick Start

Get intelligent network optimization running in minutes with automated discovery and AI-powered routing.

1. Discover Network Topology

Network Discoverybash

# Auto-discover network topology
net-optimizer discover --scan-range 192.168.0.0/16 --protocols bgp,ospf

# Analyze current routing configuration
net-optimizer analyze --baseline --performance-metrics

# Generate network map
net-optimizer topology --visualize --output network-map.html

# Identify optimization opportunities
net-optimizer assess --bottlenecks --inefficient-routes

2. Enable AI Optimization

AI Optimization Setupbash

# Enable AI-powered route optimization
net-optimizer optimize --enable-ai --learning-mode supervised

# Configure traffic analysis
net-optimizer traffic --enable-monitoring --sampling-rate 1000

# Set optimization goals
net-optimizer goals --minimize-latency --maximize-throughput --load-balance

# Start predictive optimization
net-optimizer predict --enable-congestion-prediction --forecast-window 30m

3. Monitor and Tune

Monitoring Operationsbash

# Start real-time monitoring
net-optimizer monitor --real-time --dashboard

# View optimization results
net-optimizer status --show-improvements --compare-baseline

# Generate performance report
net-optimizer report --type performance --output optimization-report.pdf

# Fine-tune AI models
net-optimizer tune --retrain-models --performance-feedback

Configuration

Configure AI NetworkOptimizer to align with your network architecture and optimization objectives.

Basic Configuration

.net-optimizer.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

network_topology:
  discovery:
    auto_discover: true
    scan_interval: "1h"
    protocols: ["bgp", "ospf", "isis"]
  devices:
    - name: "core-router-1"
      type: "router"
      management_ip: "10.0.1.1"
      protocol: "ssh"
      credentials: "{SSH_KEY_PATH}"
    - name: "core-switch-1"
      type: "switch"
      management_ip: "10.0.1.2"
      protocol: "snmp"
      community: "{SNMP_COMMUNITY}"

ai_optimization:
  machine_learning:
    enabled: true
    models: ["traffic_prediction", "congestion_avoidance", "route_optimization"]
    training_data_retention: "30d"
    retrain_interval: "24h"
  optimization_goals:
    primary: "minimize_latency"
    secondary: ["maximize_throughput", "load_balance", "minimize_cost"]
    weights:
      latency: 0.4
      throughput: 0.3
      reliability: 0.2
      cost: 0.1

traffic_analysis:
  monitoring:
    enabled: true
    sampling_rate: 1000
    flow_timeout: "60s"
    analysis_window: "5m"
  collection_methods:
    - "netflow"
    - "sflow"
    - "ebpf"
  metrics:
    - "bandwidth_utilization"
    - "packet_loss"
    - "latency"
    - "jitter"

routing_optimization:
  protocols:
    bgp:
      enabled: true
      communities: ["65001:100", "65001:200"]
      route_maps: ["OPTIMIZE_IN", "OPTIMIZE_OUT"]
    ospf:
      enabled: true
      areas: ["0.0.0.0", "0.0.0.1"]
      cost_optimization: true
  policies:
    load_balancing:
      enabled: true
      algorithm: "weighted_ecmp"
      max_paths: 4
    failover:
      enabled: true
      detection_time: "3s"
      recovery_time: "10s"

prediction_models:
  congestion_prediction:
    enabled: true
    prediction_window: "30m"
    confidence_threshold: 0.8
  traffic_forecasting:
    enabled: true
    forecast_horizon: "2h"
    seasonal_patterns: true
  failure_prediction:
    enabled: true
    predict_link_failures: true
    predict_device_failures: true

automation:
  auto_optimization:
    enabled: true
    safe_mode: true
    rollback_time: "5m"
  notifications:
    channels: ["slack", "email", "webhook"]
    events: ["optimization_applied", "congestion_predicted", "failure_detected"]

monitoring:
  dashboards:
    enabled: true
    refresh_interval: "30s"
    metrics_retention: "90d"
  alerting:
    enabled: true
    thresholds:
      latency_increase: 20
      throughput_decrease: 15
      packet_loss: 1

Traffic Analysis

AI NetworkOptimizer provides comprehensive traffic analysis using multiple collection methods and machine learning.

Flow Analysis

• NetFlow/sFlow collection
• Real-time flow analysis
• Application-aware routing
• QoS optimization

Performance Metrics

• Latency measurement
• Bandwidth utilization
• Packet loss detection
• Jitter analysis

Pattern Recognition

• Traffic pattern learning
• Anomaly detection
• Seasonal pattern analysis
• Predictive modeling

Optimization Insights

• Route efficiency analysis
• Bottleneck identification
• Capacity planning
• Performance optimization

Environment Variables

Configure AI NetworkOptimizer behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
NET_OPTIMIZER_CONFIG	Path to configuration file	.net-optimizer.yaml
NETWORK_DEVICES	Comma-separated list of network devices	auto-discover
NET_OPTIMIZER_LOG_LEVEL	Logging level (debug/info/warn/error)	info

Basic Usage

Learn the fundamental network optimization patterns and intelligent routing workflows.

Network Commands

Network Optimizationbash

# Analyze network performance and identify optimization opportunities
net-optimizer analyze --full-topology --performance-baseline

# Enable AI-powered route optimization
net-optimizer optimize --enable-ai --learning-mode --safe-rollback

# Monitor network traffic and predict congestion
net-optimizer monitor --traffic-analysis --congestion-prediction

# Generate network optimization report
net-optimizer report --performance-improvements --recommendations

CLI Commands Reference

Complete reference for all network optimization and intelligent routing commands.

optimize

Enable AI-powered network route optimization with machine learning

net-optimizer optimize [options]

Options:
  --enable-ai              Enable AI-powered optimization
  --learning-mode <mode>   Learning mode (supervised|unsupervised|reinforcement)
  --goals <goals>          Optimization goals (latency|throughput|reliability|cost)
  --protocols <protocols>  Routing protocols to optimize (bgp|ospf|isis)
  --safe-rollback         Enable automatic rollback on performance degradation
  --dry-run               Preview optimizations without applying
  --continuous            Enable continuous optimization
  --constraints <file>    Load optimization constraints from file

traffic

Analyze network traffic patterns and predict congestion

net-optimizer traffic [options]

Options:
  --enable-monitoring      Enable real-time traffic monitoring
  --sampling-rate <rate>   Traffic sampling rate (packets per second)
  --analysis-window <time> Analysis time window (5m|15m|1h)
  --flow-export <format>   Export flow data (netflow|sflow|json)
  --anomaly-detection      Enable traffic anomaly detection
  --baseline <duration>    Establish traffic baseline over duration
  --predict-congestion     Enable congestion prediction
  --export-data <file>     Export traffic analysis data

Best Practices

Network optimization best practices to maximize performance while maintaining stability.

AI-Powered Network Optimization Strategy

Start with comprehensive network discovery and baseline analysis
Enable safe mode for initial AI optimizations to prevent disruptions
Use gradual learning to train AI models on your specific network patterns
Monitor optimization impact and adjust goals based on performance
Implement automated rollback mechanisms for failed optimizations
Regularly retrain models with fresh traffic data and patterns

Route Optimization

Advanced route optimization using machine learning to automatically improve network performance.

Optimization Algorithms

Shortest Path

AI-enhanced shortest path algorithms with real-time cost updates

Load Balancing

Intelligent traffic distribution across multiple paths

Congestion Avoidance

Predictive routing to avoid congested network segments

Route Configuration

Route Optimizationbash

# Configure intelligent route optimization
net-optimizer routes --optimize-algorithm hybrid --consider-congestion

# Set up multi-path routing with AI load balancing
net-optimizer multipath --enable-ecmp --ai-load-balance --max-paths 4

# Configure traffic engineering with ML predictions
net-optimizer traffic-engineering --enable-rsvp-te --ai-bandwidth-allocation

# Optimize BGP route selection with machine learning
net-optimizer bgp --optimize-selection --ai-path-attributes --communities

Performance Tuning

Fine-tune network performance using AI insights and automated optimization techniques.

Performance Optimization

Performance Tuningbash

# Tune network performance parameters with AI optimization
net-optimizer tune --buffer-sizes --congestion-windows --qos-policies

# Optimize bandwidth allocation based on traffic patterns
net-optimizer bandwidth --ai-allocation --dynamic-adjustment --priority-classes

# Configure adaptive QoS with machine learning
net-optimizer qos --adaptive-policies --ai-classification --auto-marking

# Optimize network protocols for performance
net-optimizer protocols --tcp-optimization --udp-tuning --routing-convergence

API Integration

Integrate AI NetworkOptimizer into your network management and orchestration systems.

REST API

API Usagebash

# Trigger network optimization via API
curl -X POST https://api.augment.cfd/v1/network/optimize \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "network_id": "prod-network-01",
    "optimization_goals": ["minimize_latency", "maximize_throughput"],
    "enable_ai": true,
    "safe_mode": true
  }'

Python SDK

Python Integrationpython

from augment_network_optimizer import NetworkOptimizer

# Initialize network optimizer
optimizer = NetworkOptimizer(api_key=os.environ['AUGMENT_API_KEY'])

# Discover network topology
topology = await optimizer.discover_topology(
    scan_range='10.0.0.0/8',
    protocols=['bgp', 'ospf'],
    include_metrics=True
)

# Enable AI optimization
optimization = await optimizer.enable_ai_optimization(
    goals=['minimize_latency', 'load_balance'],
    learning_mode='supervised',
    safe_mode=True
)

print(f"Optimization enabled for {len(topology.devices)} devices")

# Monitor traffic and get predictions
traffic_analysis = await optimizer.analyze_traffic(
    duration='1h',
    enable_prediction=True,
    congestion_threshold=0.8
)

# Get optimization recommendations
recommendations = await optimizer.get_recommendations(
    network_id='prod-network-01',
    priority='high',
    include_impact_analysis=True
)

API Reference

Complete API documentation for integrating network optimization into your applications.

Network Optimization Endpoint

POST /v1/network/optimize

Enable AI-powered network optimization with intelligent routing.

Request Body:

{
  "network_config": {
    "network_id": "prod-network-01",
    "topology_discovery": {
      "auto_discover": true,
      "scan_ranges": ["10.0.0.0/8", "192.168.0.0/16"],
      "protocols": ["bgp", "ospf", "isis"]
    },
    "devices": [
      {
        "hostname": "core-router-1",
        "management_ip": "10.0.1.1",
        "device_type": "router",
        "vendor": "cisco"
      }
    ]
  },
  "optimization_settings": {
    "ai_enabled": true,
    "learning_mode": "supervised",
    "optimization_goals": [
      {
        "goal": "minimize_latency",
        "weight": 0.4
      },
      {
        "goal": "maximize_throughput",
        "weight": 0.3
      },
      {
        "goal": "load_balance",
        "weight": 0.3
      }
    ],
    "constraints": {
      "max_latency_increase": "10%",
      "min_reliability": 99.9,
      "cost_budget": 10000
    }
  },
  "traffic_analysis": {
    "enable_monitoring": true,
    "sampling_rate": 1000,
    "analysis_window": "5m",
    "prediction_enabled": true,
    "congestion_threshold": 0.8
  },
  "safety_settings": {
    "safe_mode": true,
    "rollback_timeout": "5m",
    "validation_tests": true,
    "approval_required": false
  }
}

Response:

{
  "optimization_id": "opt-12345",
  "status": "completed",
  "network_id": "prod-network-01",
  "summary": {
    "devices_optimized": 12,
    "routes_optimized": 156,
    "optimization_time": "2m 34s",
    "ai_models_applied": 3
  },
  "performance_improvements": {
    "latency_reduction": {
      "average": "23%",
      "max": "45%",
      "baseline_ms": 28.5,
      "optimized_ms": 21.9
    },
    "throughput_increase": {
      "average": "18%",
      "max": "32%",
      "baseline_mbps": 850.2,
      "optimized_mbps": 1003.7
    },
    "load_distribution": {
      "variance_reduction": "34%",
      "utilization_balance": 0.89
    }
  },
  "route_changes": [
    {
      "destination": "192.168.1.0/24",
      "old_path": ["10.0.1.1", "10.0.2.1", "10.0.3.1"],
      "new_path": ["10.0.1.1", "10.0.4.1", "10.0.3.1"],
      "improvement": {
        "latency_reduction": "15ms",
        "reason": "congestion_avoidance"
      }
    }
  ],
  "ai_insights": [
    {
      "type": "traffic_pattern",
      "insight": "High bandwidth utilization detected on link 10.0.1.1->10.0.2.1 during business hours",
      "recommendation": "Consider implementing time-based load balancing",
      "confidence": 0.92
    }
  ],
  "predictions": {
    "congestion_forecast": [
      {
        "link": "10.0.1.1->10.0.2.1",
        "predicted_congestion": "14:30-16:00",
        "probability": 0.87,
        "mitigation": "traffic_rerouting_enabled"
      }
    ]
  }
}

Troubleshooting

Common issues and solutions when implementing AI-powered network optimization.

Common Issues

Optimization Causing Performance Degradation

AI optimizations resulting in worse network performance

Solutions:

Enable safe mode with automatic rollback on performance degradation
Increase AI model training data collection period
Adjust optimization goals and constraint weights
Use gradual optimization approach instead of aggressive changes

Inaccurate Traffic Predictions

Machine learning models not accurately predicting traffic patterns

Solutions:

Increase traffic sampling rate for better data quality
Extend baseline period to capture seasonal patterns
Include more diverse traffic scenarios in training data
Tune prediction confidence thresholds based on accuracy metrics

Network Device Integration Issues

Problems connecting to or configuring network devices

Solutions:

Verify device credentials and network connectivity
Check device API versions and compatibility
Ensure required privileges for configuration changes
Test device integration with health-check command

Network Optimization Documentation Complete!

You now have comprehensive knowledge to implement AI NetworkOptimizer in your network infrastructure. From intelligent routing to predictive traffic management, you're equipped to enhance network performance with AI-powered optimization.

Ready to optimize your network with AI? Start your free network analysis today and discover how machine learning can transform your routing performance.

Installation

Deploy AI NetworkOptimizer to start intelligent network routing optimization with machine learning.

System Requirements

Linux kernel 4.9+ with eBPF support
Python 3.8+ and Node.js 16+ for AI models
Network admin privileges for routing table access
Minimum 2GB RAM (4GB recommended for large networks)
Access to network infrastructure and routing devices

Install via Package Manager

Installationbash

# Install via pip
pip install augment-network-optimizer

# Install via npm for monitoring dashboard
npm install -g @augment/network-optimizer-cli

# Install from source
git clone https://github.com/augment-ai/network-optimizer
cd network-optimizer
pip install -e .

# Install network dependencies
sudo apt-get install iproute2 iptables tcpdump

# Verify installation
net-optimizer --version

Network Infrastructure Integration

Configure integration with your network infrastructure and monitoring systems:

Network Integrationbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure network device access
export NETWORK_DEVICES="router1.company.com,switch1.company.com"
export SNMP_COMMUNITY=public
export SSH_KEY_PATH=/path/to/network-key

# Configure monitoring integration
export PROMETHEUS_URL=http://prometheus:9090
export INFLUXDB_URL=http://influxdb:8086

# Initialize network optimizer
net-optimizer init --discover-topology

# Verify network access
net-optimizer health-check --test-connectivity

Quick Start

Get intelligent network optimization running in minutes with automated discovery and AI-powered routing.

1. Discover Network Topology

Network Discoverybash

# Auto-discover network topology
net-optimizer discover --scan-range 192.168.0.0/16 --protocols bgp,ospf

# Analyze current routing configuration
net-optimizer analyze --baseline --performance-metrics

# Generate network map
net-optimizer topology --visualize --output network-map.html

# Identify optimization opportunities
net-optimizer assess --bottlenecks --inefficient-routes

2. Enable AI Optimization

AI Optimization Setupbash

# Enable AI-powered route optimization
net-optimizer optimize --enable-ai --learning-mode supervised

# Configure traffic analysis
net-optimizer traffic --enable-monitoring --sampling-rate 1000

# Set optimization goals
net-optimizer goals --minimize-latency --maximize-throughput --load-balance

# Start predictive optimization
net-optimizer predict --enable-congestion-prediction --forecast-window 30m

3. Monitor and Tune

Monitoring Operationsbash

# Start real-time monitoring
net-optimizer monitor --real-time --dashboard

# View optimization results
net-optimizer status --show-improvements --compare-baseline

# Generate performance report
net-optimizer report --type performance --output optimization-report.pdf

# Fine-tune AI models
net-optimizer tune --retrain-models --performance-feedback

Configuration

Configure AI NetworkOptimizer to align with your network architecture and optimization objectives.

Basic Configuration

.net-optimizer.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

network_topology:
  discovery:
    auto_discover: true
    scan_interval: "1h"
    protocols: ["bgp", "ospf", "isis"]
  devices:
    - name: "core-router-1"
      type: "router"
      management_ip: "10.0.1.1"
      protocol: "ssh"
      credentials: "{SSH_KEY_PATH}"
    - name: "core-switch-1"
      type: "switch"
      management_ip: "10.0.1.2"
      protocol: "snmp"
      community: "{SNMP_COMMUNITY}"

ai_optimization:
  machine_learning:
    enabled: true
    models: ["traffic_prediction", "congestion_avoidance", "route_optimization"]
    training_data_retention: "30d"
    retrain_interval: "24h"
  optimization_goals:
    primary: "minimize_latency"
    secondary: ["maximize_throughput", "load_balance", "minimize_cost"]
    weights:
      latency: 0.4
      throughput: 0.3
      reliability: 0.2
      cost: 0.1

traffic_analysis:
  monitoring:
    enabled: true
    sampling_rate: 1000
    flow_timeout: "60s"
    analysis_window: "5m"
  collection_methods:
    - "netflow"
    - "sflow"
    - "ebpf"
  metrics:
    - "bandwidth_utilization"
    - "packet_loss"
    - "latency"
    - "jitter"

routing_optimization:
  protocols:
    bgp:
      enabled: true
      communities: ["65001:100", "65001:200"]
      route_maps: ["OPTIMIZE_IN", "OPTIMIZE_OUT"]
    ospf:
      enabled: true
      areas: ["0.0.0.0", "0.0.0.1"]
      cost_optimization: true
  policies:
    load_balancing:
      enabled: true
      algorithm: "weighted_ecmp"
      max_paths: 4
    failover:
      enabled: true
      detection_time: "3s"
      recovery_time: "10s"

prediction_models:
  congestion_prediction:
    enabled: true
    prediction_window: "30m"
    confidence_threshold: 0.8
  traffic_forecasting:
    enabled: true
    forecast_horizon: "2h"
    seasonal_patterns: true
  failure_prediction:
    enabled: true
    predict_link_failures: true
    predict_device_failures: true

automation:
  auto_optimization:
    enabled: true
    safe_mode: true
    rollback_time: "5m"
  notifications:
    channels: ["slack", "email", "webhook"]
    events: ["optimization_applied", "congestion_predicted", "failure_detected"]

monitoring:
  dashboards:
    enabled: true
    refresh_interval: "30s"
    metrics_retention: "90d"
  alerting:
    enabled: true
    thresholds:
      latency_increase: 20
      throughput_decrease: 15
      packet_loss: 1

Traffic Analysis

AI NetworkOptimizer provides comprehensive traffic analysis using multiple collection methods and machine learning.

Flow Analysis

• NetFlow/sFlow collection
• Real-time flow analysis
• Application-aware routing
• QoS optimization

Performance Metrics

• Latency measurement
• Bandwidth utilization
• Packet loss detection
• Jitter analysis

Pattern Recognition

• Traffic pattern learning
• Anomaly detection
• Seasonal pattern analysis
• Predictive modeling

Optimization Insights

• Route efficiency analysis
• Bottleneck identification
• Capacity planning
• Performance optimization

Environment Variables

Configure AI NetworkOptimizer behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
NET_OPTIMIZER_CONFIG	Path to configuration file	.net-optimizer.yaml
NETWORK_DEVICES	Comma-separated list of network devices	auto-discover
NET_OPTIMIZER_LOG_LEVEL	Logging level (debug/info/warn/error)	info

Basic Usage

Learn the fundamental network optimization patterns and intelligent routing workflows.

Network Commands

Network Optimizationbash

# Analyze network performance and identify optimization opportunities
net-optimizer analyze --full-topology --performance-baseline

# Enable AI-powered route optimization
net-optimizer optimize --enable-ai --learning-mode --safe-rollback

# Monitor network traffic and predict congestion
net-optimizer monitor --traffic-analysis --congestion-prediction

# Generate network optimization report
net-optimizer report --performance-improvements --recommendations

CLI Commands Reference

Complete reference for all network optimization and intelligent routing commands.

optimize

Enable AI-powered network route optimization with machine learning

net-optimizer optimize [options]

Options:
  --enable-ai              Enable AI-powered optimization
  --learning-mode <mode>   Learning mode (supervised|unsupervised|reinforcement)
  --goals <goals>          Optimization goals (latency|throughput|reliability|cost)
  --protocols <protocols>  Routing protocols to optimize (bgp|ospf|isis)
  --safe-rollback         Enable automatic rollback on performance degradation
  --dry-run               Preview optimizations without applying
  --continuous            Enable continuous optimization
  --constraints <file>    Load optimization constraints from file

traffic

Analyze network traffic patterns and predict congestion

net-optimizer traffic [options]

Options:
  --enable-monitoring      Enable real-time traffic monitoring
  --sampling-rate <rate>   Traffic sampling rate (packets per second)
  --analysis-window <time> Analysis time window (5m|15m|1h)
  --flow-export <format>   Export flow data (netflow|sflow|json)
  --anomaly-detection      Enable traffic anomaly detection
  --baseline <duration>    Establish traffic baseline over duration
  --predict-congestion     Enable congestion prediction
  --export-data <file>     Export traffic analysis data

Best Practices

Network optimization best practices to maximize performance while maintaining stability.

AI-Powered Network Optimization Strategy

Start with comprehensive network discovery and baseline analysis
Enable safe mode for initial AI optimizations to prevent disruptions
Use gradual learning to train AI models on your specific network patterns
Monitor optimization impact and adjust goals based on performance
Implement automated rollback mechanisms for failed optimizations
Regularly retrain models with fresh traffic data and patterns

Route Optimization

Advanced route optimization using machine learning to automatically improve network performance.

Optimization Algorithms

Shortest Path

AI-enhanced shortest path algorithms with real-time cost updates

Load Balancing

Intelligent traffic distribution across multiple paths

Congestion Avoidance

Predictive routing to avoid congested network segments

Route Configuration

Route Optimizationbash

# Configure intelligent route optimization
net-optimizer routes --optimize-algorithm hybrid --consider-congestion

# Set up multi-path routing with AI load balancing
net-optimizer multipath --enable-ecmp --ai-load-balance --max-paths 4

# Configure traffic engineering with ML predictions
net-optimizer traffic-engineering --enable-rsvp-te --ai-bandwidth-allocation

# Optimize BGP route selection with machine learning
net-optimizer bgp --optimize-selection --ai-path-attributes --communities

Performance Tuning

Fine-tune network performance using AI insights and automated optimization techniques.

Performance Optimization

Performance Tuningbash

# Tune network performance parameters with AI optimization
net-optimizer tune --buffer-sizes --congestion-windows --qos-policies

# Optimize bandwidth allocation based on traffic patterns
net-optimizer bandwidth --ai-allocation --dynamic-adjustment --priority-classes

# Configure adaptive QoS with machine learning
net-optimizer qos --adaptive-policies --ai-classification --auto-marking

# Optimize network protocols for performance
net-optimizer protocols --tcp-optimization --udp-tuning --routing-convergence

API Integration

Integrate AI NetworkOptimizer into your network management and orchestration systems.

REST API

API Usagebash

# Trigger network optimization via API
curl -X POST https://api.augment.cfd/v1/network/optimize \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "network_id": "prod-network-01",
    "optimization_goals": ["minimize_latency", "maximize_throughput"],
    "enable_ai": true,
    "safe_mode": true
  }'

Python SDK

Python Integrationpython

from augment_network_optimizer import NetworkOptimizer

# Initialize network optimizer
optimizer = NetworkOptimizer(api_key=os.environ['AUGMENT_API_KEY'])

# Discover network topology
topology = await optimizer.discover_topology(
    scan_range='10.0.0.0/8',
    protocols=['bgp', 'ospf'],
    include_metrics=True
)

# Enable AI optimization
optimization = await optimizer.enable_ai_optimization(
    goals=['minimize_latency', 'load_balance'],
    learning_mode='supervised',
    safe_mode=True
)

print(f"Optimization enabled for {len(topology.devices)} devices")

# Monitor traffic and get predictions
traffic_analysis = await optimizer.analyze_traffic(
    duration='1h',
    enable_prediction=True,
    congestion_threshold=0.8
)

# Get optimization recommendations
recommendations = await optimizer.get_recommendations(
    network_id='prod-network-01',
    priority='high',
    include_impact_analysis=True
)

API Reference

Complete API documentation for integrating network optimization into your applications.

Network Optimization Endpoint

POST /v1/network/optimize

Enable AI-powered network optimization with intelligent routing.

Request Body:

{
  "network_config": {
    "network_id": "prod-network-01",
    "topology_discovery": {
      "auto_discover": true,
      "scan_ranges": ["10.0.0.0/8", "192.168.0.0/16"],
      "protocols": ["bgp", "ospf", "isis"]
    },
    "devices": [
      {
        "hostname": "core-router-1",
        "management_ip": "10.0.1.1",
        "device_type": "router",
        "vendor": "cisco"
      }
    ]
  },
  "optimization_settings": {
    "ai_enabled": true,
    "learning_mode": "supervised",
    "optimization_goals": [
      {
        "goal": "minimize_latency",
        "weight": 0.4
      },
      {
        "goal": "maximize_throughput",
        "weight": 0.3
      },
      {
        "goal": "load_balance",
        "weight": 0.3
      }
    ],
    "constraints": {
      "max_latency_increase": "10%",
      "min_reliability": 99.9,
      "cost_budget": 10000
    }
  },
  "traffic_analysis": {
    "enable_monitoring": true,
    "sampling_rate": 1000,
    "analysis_window": "5m",
    "prediction_enabled": true,
    "congestion_threshold": 0.8
  },
  "safety_settings": {
    "safe_mode": true,
    "rollback_timeout": "5m",
    "validation_tests": true,
    "approval_required": false
  }
}

Response:

{
  "optimization_id": "opt-12345",
  "status": "completed",
  "network_id": "prod-network-01",
  "summary": {
    "devices_optimized": 12,
    "routes_optimized": 156,
    "optimization_time": "2m 34s",
    "ai_models_applied": 3
  },
  "performance_improvements": {
    "latency_reduction": {
      "average": "23%",
      "max": "45%",
      "baseline_ms": 28.5,
      "optimized_ms": 21.9
    },
    "throughput_increase": {
      "average": "18%",
      "max": "32%",
      "baseline_mbps": 850.2,
      "optimized_mbps": 1003.7
    },
    "load_distribution": {
      "variance_reduction": "34%",
      "utilization_balance": 0.89
    }
  },
  "route_changes": [
    {
      "destination": "192.168.1.0/24",
      "old_path": ["10.0.1.1", "10.0.2.1", "10.0.3.1"],
      "new_path": ["10.0.1.1", "10.0.4.1", "10.0.3.1"],
      "improvement": {
        "latency_reduction": "15ms",
        "reason": "congestion_avoidance"
      }
    }
  ],
  "ai_insights": [
    {
      "type": "traffic_pattern",
      "insight": "High bandwidth utilization detected on link 10.0.1.1->10.0.2.1 during business hours",
      "recommendation": "Consider implementing time-based load balancing",
      "confidence": 0.92
    }
  ],
  "predictions": {
    "congestion_forecast": [
      {
        "link": "10.0.1.1->10.0.2.1",
        "predicted_congestion": "14:30-16:00",
        "probability": 0.87,
        "mitigation": "traffic_rerouting_enabled"
      }
    ]
  }
}

Troubleshooting

Common issues and solutions when implementing AI-powered network optimization.

Common Issues

Optimization Causing Performance Degradation

AI optimizations resulting in worse network performance

Solutions:

Enable safe mode with automatic rollback on performance degradation
Increase AI model training data collection period
Adjust optimization goals and constraint weights
Use gradual optimization approach instead of aggressive changes

Inaccurate Traffic Predictions

Machine learning models not accurately predicting traffic patterns

Solutions:

Increase traffic sampling rate for better data quality
Extend baseline period to capture seasonal patterns
Include more diverse traffic scenarios in training data
Tune prediction confidence thresholds based on accuracy metrics

Network Device Integration Issues

Problems connecting to or configuring network devices

Solutions:

Verify device credentials and network connectivity
Check device API versions and compatibility
Ensure required privileges for configuration changes
Test device integration with health-check command

Network Optimization Documentation Complete!

Ready to optimize your network with AI? Start your free network analysis today and discover how machine learning can transform your routing performance.

AI NetworkOptimizer

Smart Routing

Congestion Prediction

Performance Analytics

Auto-Configuration

Installation

System Requirements

Install via Package Manager

Network Infrastructure Integration

Quick Start

1. Discover Network Topology

2. Enable AI Optimization

3. Monitor and Tune

Configuration

Basic Configuration

Traffic Analysis

Flow Analysis

Performance Metrics

Pattern Recognition

Optimization Insights

Environment Variables

Basic Usage

Network Commands

CLI Commands Reference

optimize

traffic

Best Practices

AI-Powered Network Optimization Strategy

Route Optimization

Optimization Algorithms

Shortest Path

Load Balancing

Congestion Avoidance

Route Configuration

Performance Tuning

Performance Optimization

API Integration

REST API

Python SDK

API Reference

Network Optimization Endpoint

POST /v1/network/optimize

Request Body:

Response:

Troubleshooting

Common Issues

Optimization Causing Performance Degradation

Inaccurate Traffic Predictions

Network Device Integration Issues

Network Optimization Documentation Complete!

AI NetworkOptimizer

Smart Routing

Congestion Prediction

Performance Analytics

Auto-Configuration

Installation

System Requirements

Install via Package Manager

Network Infrastructure Integration

Quick Start

1. Discover Network Topology

2. Enable AI Optimization

3. Monitor and Tune

Configuration

Basic Configuration

Traffic Analysis

Flow Analysis

Performance Metrics

Pattern Recognition

Optimization Insights

Environment Variables

Basic Usage

Network Commands

CLI Commands Reference

optimize

traffic

Best Practices

AI-Powered Network Optimization Strategy

Route Optimization

Optimization Algorithms