Workshops Archive

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

Workshop: Sustainable Supercomputing

Authors: Roblex NANA TCHAKOUTE and Claude TADONKI (Centre de recherche en informatique (CRI), Mines Paris - PSL University)

Abstract: 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. Using our framework, we design and evaluate four novel and malleable job schedulers. Our flagship energy-aware policy, Zeus, establishes a new Pareto-optimal frontier and reduces total energy consumption by ≈8-10% compared to the SOTA performance scheduler Pollux with no statistically significant loss in system throughput. EAS-Sim is available as open-source on GitHub.

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