ACO-Optimized DRL Model for Energy-Efficient Resource Allocation in High-Performance Computing

High-Performance Computing (HPC) is a pillar in today's computational science, efficiently processing vast multi-dimensional datasets at staggering speeds. However, Resource Allocation (RA) within HPC remains a challenge. The essence lies in judiciously allocating computational resources to tasks, ensuring swift task completion, and reducing Energy Consumption (EC). Traditional methods often grapple with this balance. Machine learning, also called DL, and Deep Learning (DL) have been used in recent progress to address this unexplained phenomenon. The implementation of Deep Reinforcement Learning (DRL) for rendering real-time decisions about RA continues to add attention. However, there are instances when the DRL becomes locked in local optima and ignores ideas on a global scale. An innovative approach involving Ant Colony Optimization (ACO) with DRL is recommended to address this task. The path-finding abilities of ants proved to motivate ACO, which enhances DRL's efficiency by redirecting it against nearby devices. The decision-making problem is defined mainly in the ACO-optimized DRL hybrid model, which combines the security of DRLs and ACOs. The preliminary findings show that it performs more effectively than the other models, including conventional DRL, with improvements to response time (RT), Makespan, and other parameters. This is a key step forward for HPC-RA.