Abstract
Cloud computing has revolutionized the way computational resources are provisioned and managed, offering scalable and flexible services to meet diverse user demands. However, cost-effective resource management is a very challenging process because of the dynamism and diversity of the aspects of cloud environments that changes in terms of load and resources. The traditional sources of resource acquisition do not have the capacity to deliver the alternatives expected on their cost without having a negative impact on the performance of the resources. This work describes the new approach of utilizing the ACO for resource management in cloud computing. The method that is proposed contains the potential to incorporate pheromone-based heuristics for controlling the process of resource allocation such that reduced operational costs are ensured as well as the performance of the process is maintained at the optimal rate. ACO explains the behaviour of the search process where the allocation of the tasks is done based on the values of the pheromone trails and the heuristic information. An ACO model that includes dynamic measurements for the diverse cloud environment and several adaptive mechanisms for creating more tasks and virtual machines (VMs) can be considered a helpful solution for actual cloud applications. The results of the experiments are high in terms of cost-effectiveness compared to other approaches and reflect the ACO’s ability to function in dynamic cloud environments.
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