Switching Efficiency: A Metric Framework for AI Data Center Networks

This document specifies the Switching Efficiency Framework, a measurement methodology designed to evaluate network efficiency in AI Data Centers (AIDCs). Conventional network metrics, such as bandwidth utilization or network throughput, fail to directly link network activity to computational progress, as they cannot distinguish computationally effective data that directly advances neural network computing from the redundant traffic induced by both multi-hop forwarding and the algorithmic overhead of collective operations. To address this, this document defines the Switching Efficiency Framework, a measurement methodology for evaluating AIDC network efficiency. The core metric, Switching Efficiency, quantifies the computationally effective data throughput delivered per unit of provisioned switching capacity. To facilitate precise diagnostic analysis, the framework further decomposes this core metric into three fine-grained factors: Data Efficiency, Routing Efficiency, and Port Utilization. This framework provides metrics that can help operators identify communication bottlenecks and evaluate topology-traffic alignment.