Enhancing Load Balancing in Cloud Computing using a Hybrid Ant Earthworm Optimization Algorithm - Nampally Vijay Kumar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Information technology has significantly advanced since cloud computing was introduced. The distribution of resources from a data center involves many different factors, including the load balancing of workloads in the cloud environment. Through efficient and equitable job distribution among computing resources, load balancing increases user happiness and boosts system efficiency. Additionally, it would be challenging to maintain load balancing among resources because they are typically dispersed in a heterogeneous manner. Therefore, in this paper, a hybrid Ant earthworm optimization algorithm (AEOA) is implemented to perform effective load balancing. The earthworm optimization algorithm (EOA) was used as a local search approach to boost the Antlion optimization algorithm’s (ALO) exploitation potential and prevent it from becoming stuck in local optima. Through load balancing between the VMs, this hybridization improves the machine’s performance. Optimizing the waiting time of jobs will increase the throughput of VMs and preserve the balance between task priorities. The robustness of the algorithm will be validated by comparing the results of the proposed approach obtained from the simulation process with the existing load balancing and scheduling algorithm. All these experiments will be implemented on cloudsim