Cognitive BGP (C-BGP): AI-Driven Route Optimization for Global Internet Resilience
Keywords:
Cognitive Routing, BGP Optimization, Reinforcement Learning, Neural Path Optimization Engine, Route Convergence, AS-Path Prediction, Global Internet BackboneAbstract
The Border Gateway Protocol (BGP) forms the nervous system of the Internet, yet its convergence remains slow, policy-driven, and largely reactive. Route flaps, path inflation, and misconfigurations continue to cause global service disruptions. Traditional optimizations—route dampening, MED tuning, and manual LOCAL_PREF adjustments—lack the predictive capacity to prevent instability.
This research introduces Cognitive BGP (C-BGP), an AI-driven framework for adaptive inter-domain routing that learns optimal policy decisions from real-time telemetry. Its core innovation, the Neural Path Optimization Engine (NPOE), employs deep reinforcement learning to predict path congestion, route churn, and policy conflicts before they propagate across autonomous systems. NPOE interfaces with route reflectors and BGP speakers via NETCONF/RESTCONF APIs to dynamically adjust LOCAL_PREF, AS_PATH, and MED values without operator intervention.
Evaluations using global BGP datasets from RIPE and Route Views demonstrate that C-BGP reduces average convergence time by 58 %, lowers route-flap frequency by 47 %, and improves inter-AS path stability by 39 % compared with conventional policy automation. The approach transforms the Internet’s control plane from static configuration to cognitive adaptation—an essential step toward self-governing global routing.
