Meta-RHDC: Meta Reinforcement Learning Driven Hybrid Lyrebird Falcon Optimization for Dynamic Load Balancing in Cloud Computing

Cloud computing offers a scalable and cost-effective platform by providing on-demand access to shared computational resources. Efficient load balancing is essential to maintain optimal performance and maximize resource utilization, ensuring an even distribution of network traffic across servers, preventing overload, enhancing response times, and improving system reliability. This paper proposes a Meta Reinforcement Learning Driven Hybrid Lyrebird Falcon Optimization for Dynamic Load Balancing in Cloud Computing (Meta-RHDC), a novel approach for dynamic load balancing in cloud environments. The Meta-RHDC model leverages convolutional and recurrent neural networks to predict virtual machine loads and dynamically classify them into overloaded and underloaded categories. By integrating reinforcement learning with advanced optimization techniques, Meta-RHDC significantly improves task scheduling and load balancing compared to existing methods such as Load Optimization Algorithm (LOA), Reinforcement Learning (RL), and Falcon Optimization Algorithm (FOA). Extensive experiments conducted on the CloudSim platform demonstrate that Meta-RHDC achieves substantial improvements in key performance metrics. For task counts, makespan is reduced by 19.51%, energy consumption by 22.75%, and balanced CPU utilization by 21.98%. Resource utilization increases by 32.52%, scalability efficiency improves by 49.03%, and the failure rate decreases by 19.72%. For virtual machine (VM) counts, makespan improves by 30.57%, energy consumption by 42.59%, and balanced CPU utilization by 36.85%. Resource utilization rises by 31.75%, scalability efficiency increases by 36.61%, and the failure rate drops by 38.19%. These results confirm the robustness and efficiency of Meta-RHDC in optimizing execution, resource management, and scalability across diverse workloads, making it a superior solution for dynamic load balancing in cloud computing.