Empowering AI-Centric Workloads with Optical Intra-MEC Data Center Fabrics

Abstract

The rapid proliferation of Artificial Intelligence (AI) workloads, including Large Language Models (LLMs) and Generative AI (GAI), is fundamentally transforming Multi-access Edge Computing (MEC) infrastructures. AI-driven services deployed at the edge exhibit highly synchronized, bursty and bandwidth-intensive traffic patterns, contrasting with traditional MEC applications that generate moderate, stable flows. This evolution stresses intra-MEC network fabrics, leading to potential congestion, unfair queuing and underutilized computational resources when conventional architectures designed for elastic workloads are applied. In response, we propose an AI-aware intra-MEC data center network (DCN) architecture coupled with Wavelength Division Multiplexing (WDM)-based Medium Access Control (MAC) protocols. The proposed edge DCN enables fine-grained traffic prioritization, ensuring latencysensitive AI workloads maintain sub-us latencies while efficiently accommodating medium and low-priority applications. Through simulation-driven evaluation under realistic AI escalation scenarios, we demonstrate over 90% bandwidth utilization with near-zero packet loss, deterministic latency guarantees across heterogeneous workloads, and robust performance even under extreme AI traffic bursts. These results highlight the potential of AI-aware, intra-MEC network architectures to meet the demands of AI-centric edge environments. Ultimately, this work contributes to the realization of next-generation networks and the MEC 2.0 vision, where intelligent, high-performance edge infrastructures seamlessly support diverse, latency-sensitive workloads at scale.