Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning

TL;DR

This paper introduces Deep-EAS, a deep reinforcement learning-based scheduler that optimizes energy efficiency and performance in heterogeneous cluster systems, adapting to various workloads without prior knowledge.

Contribution

The paper presents a novel deep reinforcement learning approach for energy-aware job scheduling in heterogeneous clusters, outperforming traditional heuristics.

Findings

Deep-EAS converges rapidly in training.

Deep-EAS outperforms standard heuristics under heavy load.

The approach adapts to different workload characteristics.

Abstract

Energy consumption is one of the most critical concerns in designing computing devices, ranging from portable embedded systems to computer cluster systems. Furthermore, in the past decade, cluster systems have increasingly risen as popular platforms to run computing-intensive real-time applications in which the performance is of great importance. However, due to different characteristics of real-time workloads, developing general job scheduling solutions that efficiently address both energy consumption and performance in real-time cluster systems is a challenging problem. In this paper, inspired by recent advances in applying deep reinforcement learning for resource management problems, we present the Deep-EAS scheduler that learns efficient energy-aware scheduling strategies for workloads with different characteristics without initially knowing anything about the scheduling task at hand. Results show that Deep-EAS converges quickly, and performs better compared to standard manually-tuned heuristics, especially in heavy load conditions.