DYNAMIC LOAD BALANCING IN CLOUD COMPUTING USING HYBRID KOOKABURRA-PELICAN OPTIMIZATION ALGORITHMS
Journal Title: International Journal of Data Science and Artificial Intelligence - Year 2024, Vol 2, Issue 04
Abstract
Cloud Computing (CC) technology facilitates virtualized computer resources to users via service providers. Load balancing 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 significant challenge in cloud data centers, hindering efficient resource utilization. To address these issues, this paper proposes a novel Dynamic Efficient Load Balancing in Cloud using kookaburra Infused pelican Optimization for virtUal Server (DELICIOUS) is developed for effective load balancing process in 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 simulation experiments of the proposed 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.
Authors and Affiliations
G. Saranya, G. Belshia Jebamalar and Chukka Santhaiah
Keywords
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