Fog computing is one of the most important emerging paradigms in recent technological development. It alleviates several limitations of cloud computing by bringing computation, communication, storage, and real-time services near to the end-users. However, with the rapid development of automation in smart cities, the number of task executions by fog nodes are increasing, requiring additional fog nodes. In this paper, we present a Scheduling-based Dynamic Fog Computing (SDFC) Framework to augment the utilization of existing resources rather than adding further fog resources. It includes an additional layer, Master Fog (MF), between the cloud and general-purpose fogs, which are addressed here as Citizen Fog (CF). The MF is responsible for deciding task execution in CFs and the cloud. We use the Comparative Attributes Algorithm (CAA) to schedule tasks based on their priority and a Linear Attribute Summarized Algorithm (LASA) to select the most available CF with the highest computational ability. Our empirical results validate our SDFC framework and show the dependency on the cloud reduces by 15%–20% and overall execution time decreases by 45%–50%.

雾计算是最近技术发展中最重要的新兴范例之一。通过将计算，通信，存储和实时服务带给最终用户，它减轻了云计算的一些限制。但是，随着智能城市中自动化技术的飞速发展，雾节点执行任务的数量正在增加，因此需要更多的雾节点。在本文中，我们提出了一种基于调度的动态雾计算（SDFC）框架，以增加现有资源的利用率，而不是增加其他雾资源。它在云和通用雾之间包括一个附加层，即主雾（MF），在此将其称为“公民雾（CF）”。MF负责确定CF和云中的任务执行。我们使用比较属性算法（CAA）用于根据任务的优先级安排任务，而线性属性汇总算法（LASA）用于选择计算能力最高的可用CF。我们的经验结果验证了我们的SDFC框架，并表明对云的依赖性减少了15％–20％，总体执行时间减少了45％–50％。