Research on key technologies of optical networks for interconnection between artificial intelligent data centers

Abstract

As a critical computing infrastructure for large-scale model applications, the efficient operation of artificial intelligence data center relies on high-performance optical network transmission infrastructure. However, the optical networks interconnecting artificial intelligence data center face numerous challenges in meeting the demands of high real-time, high burstiness, and high reliability. Based on this, real-time resource allocation aims to overcome localized scheduling limitations in optical networks interconnecting artificial intelligence data center and reduce transmission and scheduling delays. Adaptive and collaborative optimization enables a continuous progression from passive adjustment to active collaboration in response to dynamic traffic bursts. Proactive failure recovery aims to achieve an orderly evolution from passive restoration to active intervention for reliability in optical networks interconnecting artificial intelligence data center. Future developments in large-scale real-time scheduling, deep computing-network convergence, and AI digital twins will drive new advancements in artificial intelligence data center interconnections.

Keywords

• artificial intelligent data center
• resource allocation
• network optimization
• failure recovery