Nature-inspired load balancing in cloud systems: a PSO-GA hybrid optimisation model

Key Points

• This research investigates a hybrid optimization model to improve load balancing in cloud computing using nature-inspired algorithms.
• Developed a hybrid optimization model combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
• Implemented simulations to assess load balancing effectiveness in cloud systems.
• Analyzed performance metrics including response time and resource utilization.
• The PSO-GA hybrid model achieved a 30% increase in load balancing efficiency compared to traditional methods.
• Response time decreased by an average of 20 ms (95% CI: 10-30 ms, p<0.01) after optimization.