AI-Driven Dynamic Path Selection
Master AI-powered path selection that continuously evaluates and predicts the quality of all available WAN circuits, making intelligent routing decisions that optimize application performance in real time.
How AI Path Selection Works
Traditional SD-WAN measures current circuit quality and switches when thresholds are breached. AI path selection goes further by predicting future circuit quality using time-series models trained on historical patterns, enabling proactive path changes before degradation affects users.
Path Selection Signals
| Signal | Measurement | AI Analysis |
|---|---|---|
| Latency | One-way and round-trip delay | Trend prediction, seasonal patterns |
| Jitter | Latency variation over time | Stability scoring, voice/video impact |
| Packet Loss | Loss percentage per interval | Burst detection, degradation prediction |
| Bandwidth | Available capacity per circuit | Utilization forecasting, congestion prediction |
| MOS Score | Computed voice quality metric | Application experience prediction |
Implementing AI Path Selection
Circuit Profiling
AI builds behavioral profiles for each WAN circuit, learning its typical performance patterns including daily cycles, peak hours, and weather-related degradation.
Application SLA Mapping
Define SLA requirements per application class (voice needs low jitter, video needs bandwidth, data tolerates latency) that guide AI routing decisions.
Predictive Modeling
ML models trained on circuit telemetry predict future quality for each path, enabling proactive routing changes before SLA violations occur.
Multi-Path Optimization
AI distributes traffic across multiple paths simultaneously (packet-level or flow-level) to maximize aggregate performance and resilience.
Continuous Learning
Models continuously update as circuit behavior changes due to provider upgrades, new traffic patterns, or environmental factors.
Advanced Path Selection Strategies
Per-Packet Steering
AI distributes individual packets across circuits for maximum throughput, using FEC (Forward Error Correction) to handle any out-of-order delivery.
Sub-Second Failover
Predictive models maintain warm standby paths that can activate in under 100ms, faster than TCP retransmission timers.
Cost-Aware Routing
AI balances circuit costs with performance requirements, preferring cheaper broadband when SLAs can be met and escalating to MPLS only when needed.
LTE/5G Augmentation
ML determines when to activate cellular backup based on predicted broadband degradation and application criticality assessment.
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