  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.
