GrapheonRL: A Graph Neural Network and Reinforcement Learning Framework for Constraint and Data-Aware Workflow Mapping and Scheduling in Heterogeneous HPC Systems

TL;DR

GrapheonRL introduces a novel framework combining Graph Neural Networks and Reinforcement Learning to improve workflow scheduling in heterogeneous HPC systems, achieving near-optimal solutions with significantly reduced computation time.

Contribution

It presents a scalable, adaptive scheduling approach that handles dynamic constraints and workload complexities better than traditional ILP and heuristic methods.

Findings

76% faster than ILP-based solutions

Comparable to heuristics in speed, only 3.85 times slower than optimal

Effectively adapts to different workflows and system constraints

Abstract

Effective resource utilization and decreased makespan in heterogeneous High Performance Computing (HPC) environments are key benefits of workload mapping and scheduling. Tools such as Snakemake, a workflow management solution, employ Integer Linear Programming (ILP) and heuristic techniques to deploy workflows in various HPC environments like SLURM (Simple Linux Utility for Resource Management) or Kubernetes. Its scheduler factors in workflow task dependencies, resource requirements, and individual task data sizes before system deployment. ILP offers optimal solutions respecting constraints, but only for smaller workflows. Meanwhile, meta-heuristics and heuristics offer faster, though suboptimal, makespan. As problem sizes, system constraints, and complexities evolve, maintaining these schedulers becomes challenging. In this study, we propose a novel solution that integrates Graph Neural Network (GNN) and Reinforcement Learning (RL) to flexibly handle workflows, dynamic constraints, and heterogeneous resources while providing quick responses. GNN manages dependencies and resource requirements, and RL optimizes scheduling decision-making via a learned policy, overcoming the need for a comprehensive global search. Experimental results with different datasets demonstrate that this method effectively adapts to different workflows, adheres to HPC constraints, and offers optimal solutions akin to ILP but with drastically reduced execution times (76 percent faster), comparable to heuristic methods (only 3.85 times slower than OLB). Our contribution is to provide a robust yet scalable mapping and scheduling solution that can handle changing constraints, as well as workload sizes and complexities in a heterogeneous HPC Compute Continuum system landscape.