Dynamic Load Balancing in Cloud Computing using Hybrid Kookaburra-Pelican Optimization Algorithms

Cloud Computing (CC) technology facilitates virtualized computing resources through service providers to users. Balancing loads assumes a critical role in distributing dynamic workloads across cloud systems, ensuring equitable resource allocation without overwhelming or underutilizing virtual machines (VMs). However, uneven workload distribution poses a major obstacle in cloud data centres, hindering the effective use of available resources. This research proposes a novel Dynamic Efficient Load Balancing in Cloud approach to overcome these problems using Kookaburra Infused pelican Optimization for virtUal Server (DELICIOUS) is developed for effective load balancing process in a cloud computing environment. The Hybrid Kookaburra-Pelican Optimization Algorithm (HK-POA) is implemented for offloading decisions which optimizes resource allocation and enhances user experiences. The evaluation of the performance of the DELICIOUS framework involves a thorough assessment that includes essential metrics such as throughput, execution time, latency, waiting time, computational complexity, and computational cost. The proposed model's simulation studies DELICIOUS framework are conducted using CloudSim and achieves a better throughput of 1206.6 Kbps whereas, the GRAF, QoDA-LB, and RATS-HM technique attains 865 Kbps, 943.4 Kbps, and 984.6 Kbps respectively for intelligent load balancing in cloud networks.