Cloud computing (CC) allows on-demand networks to access central computer resources, such as servers, databases, storage, and network services. While clouds can handle enormous amounts of data, they still encounter problems due to insufficient cloud resources. Therefore, another computing model, called fog computing, was introduced. However, the inefficient scheduling of user tasks in fog computing can cause more delays than that in CC. To address the issues of resource utilization, response time, and latency, optimal and efficient techniques are required for the scheduling strategies. In this study, we developed an extended particle swarm optimization (EPSO) algorithm with an extra gradient method to optimize the task scheduling problem in cloud-fog environments. Our primary aim is to improve the efficiency of resources and minimize the time taken to complete tasks. We conducted extensive experiments on the iFogSim simulator in terms of makespan and total cost. We compared the performance of the proposed EPSO method with that of other traditional techniques, such as ideal PSO and modified PSO; the results demonstrated that EPSO achieved a makespan of 342.53 s. Thus, it can be concluded that the performance of the proposed method is comparable to that of other approaches.

中文翻译：

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雾计算环境下基于扩展粒子群优化的资源调度优化

云计算 (CC) 允许按需网络访问中央计算机资源，例如服务器、数据库、存储和网络服务。虽然云可以处理海量数据，但由于云资源不足，它们仍然会遇到问题。因此，引入了另一种计算模型，称为雾计算。然而，雾计算中用户任务的低效调度会导致比 CC 中更多的延迟。为了解决资源利用率、响应时间和延迟问题，调度策略需要优化和高效的技术。在这项研究中，我们开发了一种扩展粒子群优化 (EPSO) 算法，使用额外的梯度方法来优化云雾环境中的任务调度问题。我们的主要目标是提高资源效率并最大限度地减少完成任务所需的时间。我们在 iFogSim 模拟器上进行了大量实验，包括制造时间和总成本。我们将所提出的 EPSO 方法的性能与其他传统技术（如理想 PSO 和改进的 PSO）的性能进行了比较；结果表明，EPSO 实现了 342.53 秒的完工时间。因此，可以得出结论，所提出的方法的性能与其他方法的性能相当。