An Efficient Nature-Inspired Optimization Method for Cloud Load Balancing for Enhanced Resource Utilization - Khan, Mohammad Imran; Sharma, Kapil (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Effective resource management is essential in the dynamic world of cloud computing to guarantee top performance and lowest costs. In order to improve cloud load balancing for better resource utilisation, this paper presents Load Balance, a revolutionary nature-inspired optimisation technique. Load Balance uses bioinspired algorithms to dynamically distribute workloads among cloud resources, drawing inspiration from the adaptive and self-organizing behaviours seen in natural ecosystems.The suggested approach repeatedly improves load distribution strategies by utilising the ideas of genetic algorithms and swarm intelligence. By use of the cooperative endeavours of virtualized entities that emulate the collective intelligence of a swarm, Load Balance adjusts instantly to fluctuating workloads, thereby reducing reaction times and optimising resource usage. Additionally, the incorporation of genetic algorithms speeds up the process of load balancing policy evolution across multiple generations, optimising the system for increased effectiveness.In-depth cloud-based simulations were carried out to verify the efficacy of load balance by contrasting its results with those of conventional load balancing techniques. The outcomes reveal that load Balance continuously outperforms previous methods, demonstrating its capacity to adjust to changing workloads while preserving improved resource usage. This optimisation technique, which draws inspiration from nature, not only advances cloud load balancing but also offers modern cloud infrastructures an effective and long-lasting solution. The study’s conclusions have a big impact on the development of cloud computing and present a viable path to better resource management and system performance overall.