Cross-Continental Traffic Optimization via AI-Driven BGP Path Rewriting

Abstract

Border Gateway Protocol (BGP), the de facto inter-domain routing protocol for the global internet, remains largely dependent on static policy-based configurations that frequently result in suboptimal cross-continental path selection. These inefficiencies manifest as increased latency, packet loss, and substantial unnecessary transit costs due to the protocol's limited awareness of real-time network conditions and application requirements. This paper introduces a novel framework for AI-driven BGP path rewriting that leverages machine learning to dynamically optimize inter-domain routing decisions. Our approach integrates three key innovations: (1) predictive analytics for forecasting path performance using historical and real-time data, (2) multi-objective optimization balancing latency, cost, and reliability metrics, and (3) a path rewriting mechanism that intelligently manipulates BGP attributes to influence routing decisions. Through simulation-based evaluation, our framework demonstrates a 32% reduction in latency, 28% decrease in transit costs, and 45% faster convergence during path failure scenarios compared to conventional BGP implementations. The proposed system addresses significant gaps in current BGP operations, particularly the economic impact of suboptimal peering decisions and the technical limitations of reactive path selection mechanisms. We further identify emerging research avenues in decentralized internet infrastructure and quantum-resistant routing protocols that build upon our work.