EAS-Sim: A Framework and its Methodology for the Co-Design of Multi-Objective, Energy-Aware Schedulers for AI Clusters

The explosive growth of large-scale Deep Learning (DL) models has made energy consumption a first-order operational cost and constraint in modern High-Performance Computing (HPC) datacenters. Existing DL schedulers, however, are largely single-objective and energy oblivious, struggling to balance the competing demands of performance, fairness, and Quality of Service (QoS). To address this flaw, we propose a methodology for the co-design of multi-objective and energy-aware schedulers together with the associated simulation framework, the so-called EAS-Sim. Our methodology stands as a systematic approach to enhance State-of-the-Art (SOTA) scheduling heuristics with energy-efficiency objectives.