AI-Driven Network Traffic Optimization and Fault Detection in Enterprise WAN

In the contemporary landscape of enterprise-Wide Area Networks (WANs), managing complex interconnections between multiple data centers and branch offices poses significant challenges. This paper explores an innovative AI-driven approach to network traffic optimization and fault detection, utilizing knowledge graphs to enhance network performance and reliability. The proposed framework integrates real-time data collection, reinforcement learning algorithms, and graph-based machine learning to dynamically optimize traffic routing while ensuring low latency and high availability for critical applications such as financial transactions and video conferencing. A detailed knowledge graph representation is introduced, capturing essential network elements, including devices, links, traffic flows, policies, faults, and geographical regions. This facilitates a comprehensive understanding of the intricate relationships within the network infrastructure. Cypher queries are employed to extract relevant insights from the knowledge graph, enabling proactive fault detection and routing optimization based on historical patterns and current network conditions. The methodology emphasizes dynamic route adjustments based on real-time telemetry, minimizing disruption during link failures. Additionally, predictive modeling leverages historical fault data to forecast potential future issues, allowing for preemptive measures to maintain operational integrity. The benefits of this AI-driven approach include real-time traffic optimization, proactive fault management, compliance with security and QoS policies, and scalability to accommodate network growth. Overall, this research demonstrates how combining AI algorithms with knowledge graph models can revolutionize wired network management, significantly improving the resilience and efficiency of enterprise WANs.