Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient task scheduling in Cloud Computing (CC) remains a critical challenge due to the need to balance energy consumption and deadline adherence. Existing scheduling approaches often suffer from high energy consumption and inefficient resource utilization, failing to meet stringent deadline constraints, especially under dynamic workload variations. To address these limitations, this study proposes an Energy-Deadline Aware Task Scheduling using the Water Wave Optimization (EDATSWWO) algorithm. Inspired by the propagation and interaction of water waves, EDATSWWO optimally allocates tasks to available resources by dynamically balancing energy efficiency and deadline adherence. The algorithm evaluates tasks based on their energy requirements and deadlines, assigning them to virtual machines (VMs) in the multi-cloud environment to minimize overall energy consumption while ensuring timely execution. Google Cloud workloads were used as the benchmark dataset to simulate real-world scenarios and validate the algorithm's performance. Simulation results demonstrate that EDATSWWO significantly outperforms existing scheduling algorithms in terms of energy efficiency and deadline compliance. The algorithm achieved an average reduction of energy consumption by 21.4%, improved task deadline adherence by 18.6%, and optimized resource utilization under varying workloads. This study highlights the potential of EDATSWWO to enhance the sustainability and efficiency of multi-cloud systems. Its robust design and adaptability to dynamic workloads make it a viable solution for modern cloud computing environments, where energy consumption and task deadlines are critical factors.

Scheduling is an NP-hard problem, and metaheuristic algorithms are often used to find approximate solutions within a feasible time frame. Existing metaheuristic algorithms, such as ACO, PSO, and BOA address this problem either in cloud or fog environments. However, when these environments are combined into a hybrid cloud-fog environment, these algorithms become inefficient due to inadequate handling of local and global search strategies. This inefficiency leads to suboptimal scheduling across the cloud-fog environment because the algorithms fail to adapt effectively to the combined challenges of both environments. In our proposed Improved Butterfly Optimization Algorithm (IBOA), we enhance adaptability by dynamically updating the computation cost, communication cost, and total cost, effectively balancing both local and global search strategies. This dynamic adaptation allows the algorithm to select the best resources for executing tasks in both cloud and fog environments. We implemented our proposed approach in the CloudSim simulator and compared it with traditional algorithms such as ACO, PSO, and BOA. The results demonstrate that IBOA offers significant reductions in total cost, communication cost, and computation cost by 19.65%, 18.28%, and 25.41%, respectively, making it a promising solution for real-world cloud-fog computing (CFC) applications.

Cloud-fog computing frameworks are innovative frameworks that have been designed to improve the present Internet of Things (IoT) infrastructures. The major limitation for IoT applications is the availability of ongoing energy sources for fog computing servers because transmitting the enormous amount of data generated by IoT devices will increase network bandwidth overhead and slow down the responsive time. Therefore, in this paper, the Butterfly Spotted Hyena Optimization algorithm (BSHOA) is proposed to find an alternative energy-aware task scheduling technique for IoT requests in a cloud-fog environment. In this hybrid BSHOA algorithm, the Butterfly optimization algorithm (BOA) is combined with Spotted Hyena Optimization (SHO) to enhance the global and local search behavior of BOA in the process of finding the optimal solution for the problem under consideration. To show the applicability and efficiency of the presented BSHOA approach, experiments will be done on real workloads taken from the Parallel Workload Archive comprising NASA Ames iPSC/860 and HP2CN (High-Performance Computing Center North) workloads. The investigation findings indicate that BSHOA has a strong capacity for dealing with the task scheduling issue and outperforms other approaches in terms of performance parameters including throughput, energy usage, and makespan time.