Enhancing Resource Utilization and Load Distribution with ACO and Reinforcement Learning in Dynamic Computing Infrastructures

In the rapidly evolving landscape of dynamic computing infrastructures, efficient resource utilization and adaptive load balancing are critical for maintaining system performance and sustainability. This research introduces a novel framework that integrates Ant Colony Optimization (ACO) and Reinforcement Learning (RL) to enhance resource utilization and load distribution in such environments. In this research, we present a comprehensive framework for enhancing resource utilization and load distribution in dynamic computing infrastructures using a hybrid approach that integrates Ant Colony Optimization (ACO) and Reinforcement Learning (RL) algorithms. The proposed framework aims to address the challenges of adaptive load balancing and efficient resource management in highly variable and resource-intensive computing environments. Our approach leverages the strengths of ACO in discovering optimal paths and RL in learning from the environment to make informed decisions. We evaluate the performance of our framework using key parameters: Total Energy Consumption (kWh), Average Energy Consumption per Node (kWh), Peak Energy Consumption (kW), Energy Efficiency (Tasks/kWh), and Dynamic Energy Consumption (kWh/hour). The evaluation compares three methods: Least Load Balancing (LLB), ACO, and RL, with RL demonstrating the best results. Experimental results indicate that the RL-based approach significantly reduces Total Energy Consumption and Average Energy Consumption per Node while maintaining a lower Peak Energy Consumption. Furthermore, the RL method shows improved Energy Efficiency and optimal Dynamic Energy Consumption, highlighting its potential for sustainable and efficient resource management in dynamic computing infrastructures. This study underscores the importance of intelligent load balancing and resource optimization strategies in modern computing environments and demonstrates the effectiveness of combining ACO and RL techniques to achieve these goals. Our findings provide valuable insights for future research and development of advanced load balancing frameworks that can adapt to the ever-evolving demands of dynamic computing systems.