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

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 graphbased 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.