2019/12/11 by Amirhossein Esmaili, Massoud Pedram, Esmaili, Amirhossein +1
Computer Science · #Cloud Computing and Resource Management #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Parallel #and Cluster Computing (cs.DC) #cs.DC #cs.LG
paper · pdf · doi:10.48550/arxiv.1912.05160
Accepted in International Symposium on Quality Electronic Design (ISQED), 2020
arxiv created 2019/12/11 · openalex publication_date 2019/12/11 · arxiv updated 2019/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.