Qin: Unified Hierarchical Cluster-Node Scheduling for Heterogeneous Datacenters

Abstract

Energy efficiency is among the most important challenges for computing. There has been an increasing gap between the rate at which the performance of processors has been improving and the lower rate of improvement in energy efficiency. This paper answers the question of how to reduce energy usage in heterogeneous datacenters. It proposes a unified hierarchical scheduling using a D-Choices technique, which considers interference and heterogeneity. Heterogeneity comes from servers’ continuous upgrades and the integrated high-performance “big” and energy-efficient “little” cores. This results in datacenters becoming more heterogeneous and traditional job scheduling algorithms become suboptimal. To this end, we present a two-level hierarchical scheduler for datacenters that exploits increased server heterogeneity. It combines in a unified approach cluster and node level scheduling algorithms, and it can consider specific optimization objectives including job completion time, energy usage, and energy-delay-product (EDP). Its novelty lies in the unified approach and in modeling interference and heterogeneity. Experiments on a research cluster found that the proposed approach outperforms state-of-the-art schedulers by around 10% in job completion time, 39% in energy usage, and 42% in EDP. This paper demonstrated a unified approach as a promising direction in optimizing energy and performance for heterogeneous datacenters.