Autoscaling Enabled Intelligent Load Balancing In Cloud Computing

Cloud computing environments demand robust and intelligent task management to ensure scalability, efficiency, and cost-effectiveness. This paper introduces an Autoscaling Enabled Intelligent Load Balancing System designed for dynamic cloud environments. The system is divided into four key modules: task classification using XGBoost, resource prediction via XGBoost Regression, optimal load balancing using Particle Swarm Optimization (PSO), and adaptive autoscaling with fuzzy logic. Evaluation of each module demonstrated significant improvements in task distribution accuracy, resource utilization, and system stability. The integration of machine learning and heuristic algorithms ensures a dynamic, responsive, and energy-efficient cloud environment.