DarkTE: Towards Dark Traffic Engineering in Data Center Networks with Ensemble Learning
Renhai Xu, Wenxin Li, Keqiu Li, Xiaobo Zhou, Heng Qi
Abstract
Over the last decade, traffic engineering (TE) has always been a research hotspot in data center networks. For routing flows efficiently and practically, existing TE schemes explore experience-driven heuristics or machine learning (ML) techniques to predict/identify network flows’ size information. However, these TE schemes have significant limitations: they either identify the flow size information too late or are unaware of the ML models’ prediction errors. In this paper, we present DarkTE, a novel TE solution that can learn to predict flow size information timely for achieving better routing performance while being robust to the prediction errors. At its heart, DarkTE employs an ensemble learning technique (i.e., random forest) to classify flows into mice and elephant flows with high accuracy. It then leverages a confidence-based rate allocation and path selection scheme to mitigate the occasional classification errors. Large-scale simulations demonstrate that DarkTE classifies flows within hundreds of microseconds, and the classification accuracy is at least 86.4% over three different realistic workloads. Further, DarkTE completes flows 2.94 times faster on average and makes more links to experience over 90% bandwidth utilization than the Hedera solution.