Cloud-fog computing frameworks are emerging paradigms developed to add benefits to the current Internet of Things (IoT) architectures. In such frameworks, task scheduling plays a key role, and the optimized schedule of IoT task requests can improve system performance and productivity. In this paper, we developed an alternative task scheduling technique for IoT requests in a cloud-fog environment based on a modified artificial ecosystem-based optimization (AEO), called AEOSSA. This modification is developed using the operators of the Salp Swarm Algorithm (SSA) in an attempt to enhance the exploitation ability of AEO during the process of finding the optimal solution for the problem under consideration. The performance of the designed AEOSSA approach to tackling the task scheduling problem is evaluated using different synthetic and real-world datasets of different sizes. In addition, a comparison is conducted between AEOSSA and other well-known metaheuristic methods for performance investigation. The experimental results demonstrate the high ability of AEOSSA to tackle the task scheduling problem and perform better than other methods according to the performance metrics such as makespan time and throughput.

云雾计算框架是新兴范式，旨在为当前的物联网 (IoT) 架构增加优势。在这样的框架中，任务调度起着关键作用，物联网任务请求的优化调度可以提高系统性能和生产力。在本文中，我们基于改进的基于人工生态系统的优化 (AEO)，为云雾环境中的 IoT 请求开发了一种替代任务调度技术，称为 AEOSSA。此修改是使用 Salp Swarm 算法 (SSA) 的算子开发的，旨在在为所考虑的问题寻找最佳解决方案的过程中增强 AEO 的开发能力。使用不同大小的不同合成和现实世界数据集评估设计的 AEOSSA 方法解决任务调度问题的性能。此外，还对 AEOSSA 与其他著名的元启发式性能调查方法进行了比较。实验结果表明，AEOSSA 在解决任务调度问题方面具有很高的能力，并且根据制造时间和吞吐量等性能指标，其性能优于其他方法。