Cloud-Network Collaborative Architecture Design and Performance Optimization Based on SRv6 and Intent-Driven Approach

Abstract

Efficient cloud-network collaboration requires intelligent service orchestration, adaptive routing, and dynamic resource scheduling in distributed environments. This study proposes an intent-driven cloud-network collaborative architecture based on Segment Routing over IPv6 (SRv6). The architecture integrates intent parsing, intelligent control, and SRv6 forwarding mechanisms to achieve automated service-to-policy mapping and adaptive path orchestration. Reinforcement-learning-based routing optimization and real-time network-state feedback mechanisms are incorporated to improve resource utilization and service deployment efficiency. Experimental evaluation demonstrates significant reductions in latency and improvements in automation and resource utilization. The framework provides an effective solution for programmable networking and cloud-edge collaboration.