Bio-Inspired Adaptive Starfish Optimization Approach to Efficient Load Balancing in Cloud Computing

Effective load balancing is essential in cloud computing to optimize resource consumption, reduce reaction time, and improve system reliability. Conventional methods frequently falter under fluctuating workloads, resulting in suboptimal task distribution and heightened energy usage. This work presents an Improved Starfish Optimization Algorithm (ISFO) for cloud load balancing, motivated by the regenerative flexibility of starfish. The algorithm adaptively reallocates tasks based on real-time variations, guaranteeing optimal workload distribution and energy efficiency. The performance assessment of ISFO in comparison to Particle Swarm Optimization and Artificial Bee Colony reveals a 28.5% decrease in response time, a 48.5% enhancement in load variance, and a 25% reduction in energy consumption. These findings confirm ISFO as a scalable and efficient method for managing cloud workloads, improving overall system performance while decreasing operational expenses.