A “Big-Spine” Abstraction: Flow Prioritization With Spatial Diversity in The Data Center Network

Data center networks undergo the coexistence of latency-sensitive mice flows and bandwidth-intensive elephant flows. Jointly optimizing the performance of both traffic classes poses complex challenges. Existing flow schedulers either rely on detailed flow size information or require numerous physical priority queues (PQs) within network switches, thus facing practical challenges. In this work, we propose a novel flow scheduling algo- rithm, namely Multi-Path Multi-Level Feedback Queueing (MP- MLFQ), to overcome these limitations. MP-MLFQ leverages the spatial diversity and regularity of DCNs to realize a scheduler with numerous logical priority levels while occupying as low as 2 physical PQ at each switch port. We designed MP-MLFQ to run atop modern programmable networks, and highlighted how to implement it without modifications at the end-hosts’ stacks. Our simulation results show that MP-MLFQ outperforms existing flow size-agnostic solutions in minimizing the flow completion time, when only two PQs are available.