Enhancing Wide-Area Network Efficiency for Intelligent Computing Through SRv6-Based Path Optimization and Dynamic Load Balancing

This article presents an SRv6-based network scheme designed to enhance intelligent computing service flows, optimizing resource management in AI data centers and large model training scenarios. Our architecture integrates Layer 3 Ethernet Virtual Private Network (L3EVPN) over SRv6 with both bandwidth and policy management, dynamically adjusting bandwidth and routing to accommodate various business requirements. We establish SRv6 tunnels at the underlay level, enabling real-time path optimization and bandwidth reservation without the need for centralized control. By employing high-bandwidth routing and load balancing, we improve Wide Area Network (WAN) throughput and manage traffic efficiently. Congestion is mitigated through device-based detection and acknowledgment-assisted rate adjustment. The “dual-send selective-receive” approach ensures effective packet replication and deduplication, thereby enhancing service reliability. Overall, our SRv6-driven approach provides a robust, efficient, and secure network foundation for intelligent computing services.