HARMONIC: Uncertainty-Aware Multi-Objective Optimization for Energy-Efficient HPC Resource Management

Abstract

Exascale high-performance computing (HPC) systems face critical resource management challenges such as massive energy consumption in megawatts per facility, performance variability for identical jobs, and resource utilization inefficiencies. Traditional single-objective schedulers cannot address these multifaceted challenges effectively. This paper introduces HARMONIC (Holistic Adaptive Resource Management Optimizing Next-generation Interconnected Computing), a novel framework that simultaneously optimizes performance, energy efficiency, and resilience through uncertainty-aware multi-objective optimization. Our approach distinguishes aleatoric uncertainty (inherent system variability) from epistemic uncertainty (modeling limitations) using Bayesian neural networks and employs graph-based representations to capture complex system dependencies. Experimental validation in both simulated environments and controlled testbeds demonstrates significant improvements over state-of-the-art schedulers: 10–19% energy reduction, 16–25% throughput improvement and 18–32% performance variability reduction. These results translate to potential annual savings of multimillion dollars per exascale facility while enhancing scientific productivity through improved experimental reproducibility.