Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO’s potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique’s performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.

In the context of Cloud-Fog Computing (CFC) efficient Task Scheduling (TS) plays a critical role in optimizing resource allocation and meeting the increasingly complex performance demands of modern computing infrastructures. This study introduces an advanced iteration of the Mexican Axolotl Optimization (MAO) algorithm, a bio-inspired metaheuristic designed to address the intricate challenges of task scheduling in dynamic and distributed computing environments. The MAO algorithm presented here incorporates advanced heuristics, self-optimization mechanisms, and innovative features to significantly improve task scheduling processes. By imitating the axolotl's capacity for self-healing, the algorithm offers a solution that is both robust and adaptable to dynamic computing conditions, making it well-suited to the challenges posed by CFC. The primary objectives of the MAO algorithm are to improve resource allocation, reduce latency, and enhance overall operational efficiency in cloud and fog computing ecosystems. It achieves this by optimizing the allocation of computing tasks to available resources, taking into account factors such as makespan time, success rate and response time, while dynamically adapting the fluctuations in workloads and resource availability. By enhancing resource utilization and reducing response time, this algorithm is poised to make significant contributions to the efficiency, reliability, and overall performance of CFC infrastructures. The experimental findings indicate that the proposed algorithm exhibits superior performance in terms of makespan and response time reduced by 28% and 36%, success rate increased by 31 % and compared to alternative approaches.

Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency.