🤖 AI Summary
This work proposes a resource-aware efficiency metric for interconnection networks that explicitly accounts for the hardware overhead required to sustain non-blocking communication—specifically, link cost (α), crossbar cost (β), and concentration ratio—rather than focusing solely on latency or throughput. By modeling the impact of traffic hop count and router radix on these costs, the study systematically evaluates the cost optimality of various topologies under non-blocking constraints. It reveals that direct networks with high radix are superior for small to medium scales, whereas indirect topologies such as fat trees become necessary at large scales to manage router complexity. Furthermore, the analysis demonstrates that multi-plane star architectures achieve efficient fault tolerance with lower resource overhead compared to topologies relying on structural redundancy.
📝 Abstract
In modern network design,"efficiency"is often conflated with raw performance metrics like latency or aggregate throughput. This paper proposes a resource-centric definition of efficiency, isolating the hardware cost required to maintain a non-blocking throughput constraint. By modeling network cost as a function of the Traffic Multiplier (Hop Count) and Router Complexity (Radix), we demonstrate that the optimal topology is determined by the technological ratio between link interface costs ($\alpha$), crossbar switching costs ($\beta$), and the network concentration ratio. We conclude that while high-radix direct networks optimize efficiency at small to medium scales, indirect networks (e.g., Fat Trees) are required to cap router complexity at massive scales. Furthermore, we posit that redundancy is most efficiently handled via parallel network instances (e.g., multi-plane Star networks) rather than intrinsic topological path diversity.