Less is More: Learning Simplicity in Datacenter Scheduling

Abstract

In this paper, we present a new scheduling algorithm, Qin2, for heterogeneous datacenters. Its goal is to improve performance measured as jobs completion time by exploiting increased server heterogeneity using deep neural network (DNN) models. The proposed scheduling framework uses an efficient automatic feature selection technique, which significantly reduces the training data size required to train the DNN to levels that provide satisfactory prediction accuracy. Its efficiency is especially helpful when the DNN model is re-trained to adapt it to new types of application workloads arriving to the datacenter. The novelty of the proposed scheduling approach lies in this feature selection technique and the integration of simple and training-efficient DNN models into a scheduler, which is deployed on a real cluster of heterogeneous nodes. Experiments demonstrate that the Qin2 scheduler outperforms state-of-the-art schedulers in terms of jobs completion time.