Energy and Resource Aware Scheduling in Cloud-Fog Environment using Advanced Meta Heuristic Algorithm

For efficient management of energy and resources, especially in Cloud-Fog Computing (CFC) environment that is constantly changing and is on the rise, effective scheduling of tasks plays a crucial role. The research in this paper proposes a new approach of utilizing the Honey Badger Algorithm (HBA) for Energy and Resource aware scheduling in a CFC environment. The HBA mimics the honey badger’s foraging strategy and anyone who has observed a honey badger foraging recognizes that the algorithm is a fair representation; the agent is exploring as well as exploiting, finding the best areas and ensuring minimal energy expenditure. The proposed method prioritizes tasks based on their energy demands and resource requirements, dynamically adjusting schedules to account for varying workloads and environmental conditions. Extensive simulations demonstrate that the HBA outperforms existing algorithms PSO, GA, and IDOA in terms of energy consumption reduced by 15.6%, optimizes the resource utilization by 18.2%, task completion time improved by 17.4%, and maximizes the throughput by 21.2% making it a promising solution for sustainable and scalable CFC environments.