@article {10.3844/jcssp.2025.432.443,
article_type = {journal},
title = {FedLoBA-1: A Load Balancing Architecture for Mitigating Resource Overloading in Federated Cloud Infrastructures},
author = {Akinola, Damola Gideon and Adetiba, Emmanuel and Abayomi, Abdultaofeek and Thakur, Surendra and Nnaji, Uche and Moyo, Sibusiso},
volume = {21},
number = {2},
year = {2025},
month = {Jan},
pages = {432-443},
doi = {10.3844/jcssp.2025.432.443},
url = {https://thescipub.com/abstract/jcssp.2025.432.443},
abstract = {In a subscription-based service such as cloud computing, clients have scheduled access to shared resources such as data, software, storage, and other assets as needed. Despite several benefits, cloud computing still faces significant difficulties. Load balancing, which is the capacity of the cloud infrastructure to equally distribute tasks among the resources in the cloud environment has significant issues. Cloud federation is a novel concept in cloud deployment that was developed to overcome load imbalance and other drawbacks that come with standalone clouds. However, in a federated cloud system, effective workload sharing among participating Cloud Service Providers (CSP) is also challenging. Therefore, this study presents a Federated Load Balancing Architecture version 1 (FedLoBA-1) for optimal distribution of inter-cloud and intra-cloud loads within federated cloud infrastructures. The inter-cloud load balancing was realized using Ant Colony Optimization (ACO) whereas the intra-cloud component was realized with the Throttled algorithm. The implementation of the FedLoBA-1 and simulation of the federated cloud were carried out using the CloudAnalyst simulation toolkit. Experimental results show that FedLoBA-1 gave an average response time of 92.33 ms as compared with 328.4ms and 176.55 ms for Closest Datacenter (CDC) and Optimize Response Time (ORT) algorithms respectively. The minimum average processing time obtained for FedLoBA-1, CDC, and ORT were 1.49, 17.00, and 6.68 ms respectively. FedLoBA-1 is a valuable solution for effective resource utilization in federated cloud environments. It significantly improves load balancing in cloud federation by offering an optimized two-tiered approach for intra-cloud and inter-cloud load distribution. This approach results in significantly better performance than existing algorithms.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}