Fog computing has emerged as a complementary solution to address the issues faced in cloud computing. While fog computing allows us to better handle time/delay-sensitive Internet of Everything (IoE) applications (e.g. smart grids and adversarial environment), there are a number of operational challenges. For example, the resource-constrained nature of fog-nodes and heterogeneity of IoE jobs complicate efforts to schedule tasks efficiently. Thus, to better streamline time/delay-sensitive varied IoE requests, the authors contributes by introducing a smart layer between IoE devices and fog nodes to incorporate an intelligent and adaptive learning based task scheduling technique. Specifically, our approach analyzes the various service type of IoE requests and presents an optimal strategy to allocate the most suitable available fog resource accordingly. We rigorously evaluate the performance of the proposed approach using simulation, as well as its correctness using formal verification. The evaluation findings are promising, both in terms of energy consumption and Quality of Service (QoS).

雾计算已成为解决云计算所面临问题的补充解决方案。尽管雾计算使我们能够更好地处理时间/延迟敏感的万物互联（IoE）应用程序（例如，智能电网和对抗性环境），但仍存在许多运营挑战。例如，雾节点的资源受限性质和IoE作业的异质性使得有效安排任务的工作变得复杂。因此，为了更好地简化对时间/延迟敏感的各种IoE请求，作者通过在IoE设备和雾节点之间引入智能层以结合基于智能和自适应学习的任务调度技术做出了贡献。具体来说，我们的方法分析了IoE请求的各种服务类型，并提出了一种最佳策略来相应地分配最合适的可用雾资源。我们使用仿真来严格评估所提出方法的性能，以及使用形式验证来评估其正确性。评估结果在能源消耗和服务质量（QoS）方面都是有希望的。