A resource optimization scheduling model and algorithm for heterogeneous computing clusters based on GNN and RL

In the realm of heterogeneous computing, the efficient allocation of resources is pivotal for optimizing system performance. However, user-submitted tasks are often complex and have varied resource demands. Moreover, the dynamic nature of resource states in such platforms, coupled with variations in resource types and capabilities, results in significant intricacy of the system environment. To this end, we propose a scheduling algorithm based on hierarchical reinforcement learning, namely MD-HRL. Such an algorithm could simultaneously harmonize task completion time, device power consumption, and load balancing. It contains a high-level agent (H-Agent) for task selection and a low-level agent (L-Agent) for resource allocation. The H-Agent leverages multi-hop attention graph neural networks (MAGNA) and one-dimensional convolutional neural networks (1DCNN) to encode the information of tasks and resources. Kolmogorov–Arnold networks is then employed for integrating these representations while calculating subtask priority scores. The L-Agent exploits a double deep Q network to approximate the best strategy and objective function, thereby optimizing the task-to-resource mapping in a dynamic environment. Experimental results demonstrate that MD-HRL outperforms several state of the art baselines. It reduces makespan by 12.54%, improves load balancing by 5.83%, and lowers power consumption by 6.36% on average compared with the suboptimal method.