School of Computer Science and Engineering, VIT-AP University, Amaravathi 522237, Andhra Pradesh, India.
Sensors (Basel). 2023 Feb 22;23(5):2445. doi: 10.3390/s23052445.
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.
云雾计算是一种广泛的服务环境，旨在为客户提供快速、灵活的服务，而物联网（IoT）的迅猛发展每天都会产生大量数据。为了完成任务并满足服务级别协议（SLA）的承诺，提供商将分配适当的资源并采用调度技术来有效地管理在雾或云系统中接收的 IoT 任务的执行。云服务的有效性受到一些其他重要标准的直接影响，例如能源使用和成本，而许多现有方法并未考虑到这些标准。为了解决上述问题，需要有效的调度算法来调度异构工作负载并提高服务质量（QoS）。因此，本文提出了一种基于自然启发的多目标任务调度算法，称为电蚯蚓优化算法（EEOA），用于云雾框架中的 IoT 请求。该方法是通过将蚯蚓优化算法（EOA）和电鱼优化算法（EFO）相结合创建的，以提高 EFO 在寻找手头问题最佳解决方案时的开发潜力。针对执行时间、成本、完成时间和能耗，使用 CEA-CURIE 和 HPC2N 等真实工作负载实例对所提出的调度技术的性能进行了评估。基于仿真结果，我们的方法在考虑到不同基准的情况下，与现有算法相比，在效率方面提高了 89%，在能源消耗方面提高了 94%，在总成本方面提高了 87%。详细的仿真表明，与现有调度技术相比，所提出的方法提供了更好的调度方案和更好的结果。
