Tearlach Magri and Rebecca Camilleri, University of Malta, Malta

Cloud computing provides scalable, on-demand resources that support a wide range of servicesand applications. Efficient load balancing in cloud environments is critical when maintaining performance and quality of service. A hybrid Ant Colony Optimisation – Genetic Algorithm (ACO-GA) method is proposed for task scheduling in a hybrid cloud, implemented and evaluated using the CloudAnalyst simulator. The custom algorithm leverages ACO’s rapid local search for assigning workloads to virtual machines and GA’s global evolutionary search to diversify solutions. The ACO-GA is compared against Round Robin, pure ACO and pure GA strategies. Performance is measured by overall response time and data centre processing time. Simulation results indicate that the proposed ACO-GA outperforms the baseline strategies in both response time and data centre processing time, demonstrating that combining ACO’s pheromone-guided optimisation and GA’s genetic exploration leads to more balanced loads

Cloud Computing, Load Balancing, Round Robin, Ant Colony Optimisation, Genetic Algorithm