Machine Learning-Based Multi-Objective Optimization for HPC Workload Scheduling: A GNN-RL Approach

As high-performance computing (HPC) systems achieve exascale capabilities, traditional single-objective schedulers that optimize solely for performance prove inadequate for environments requiring simultaneous optimization of energy efficiency and system resilience. Current scheduling approaches result in suboptimal resource utilization, excessive energy consumption, and reduced fault tolerance in the demanding requirements of large-scale scientific applications. This dissertation proposes a novel multi-objective optimization framework that integrates graph neural networks (GNNs) with reinforcement learning (RL) to jointly optimize performance, energy efficiency, and system resilience in HPC workload scheduling. The central hypothesis posits that graph-structured representations of workloads and system states, combined with adaptive learning policies, can significantly outperform traditional scheduling methods in complex, dynamic HPC environments. The proposed framework comprises three integrated components: (1) GNN-RL, which combines graph neural networks with reinforcement learning for adaptive policy development; (2) EA-GATSched, an energy-aware scheduler leveraging Graph Attention Networks; and (3) HARMONIC (Holistic Adaptive Resource Management for Optimized Next-generation Interconnected Computing), a probabilistic model for workload uncertainty quantification. The proposed methodology encompasses novel uncertainty modeling techniques, scalable GNN-based scheduling algorithms, and comprehensive empirical evaluation using production supercomputing workload traces. Preliminary results demonstrate 10-19% improvements in energy efficiency while maintaining comparable performance metrics. The framework will be evaluated across makespan reduction, energy consumption, resource utilization efficiency, and fault tolerance in various operational scenarios. This research advances sustainable and resilient HPC resource management, providing critical infrastructure support for next-generation scientific computing applications.