Heterogeneous Computational Scheduling Using Adaptive Neural Hyper-Heuristic

Abstract

In heterogeneous computing environments, efficiently scheduling tasks, especially those forming Directed Acyclic Graphs (DAGs), is critical. This is particularly true for various Cloud and Edge computing tasks, as well as training Large Language Models (LLMs). This paper introduces a new scheduling approach using an Adaptive Neural Hyper-heuristic. By integrating a neural network trained with genetic algorithms, our method aims to minimize makespan. The approach uses a two-level algorithm: the first level prioritizes tasks using adaptive heuristic and the second level assigns resources based on the Earliest Finish Time (EFT) algorithm. Our tests show that this method significantly improves over traditional scheduling heuristics and other machine learning-based approaches. It reduces the makespan by 6.7% for small-scale DAGs and 28.49% for large-scale DAGs compared to the leading DONF algorithm. Additionally, it achieves a proximity of 84.08% to 96.43% to the optimal solutions found using Mixed-Integer Linear Programming (MILP), demonstrating its effectiveness in diverse computational settings.