Cloud computing has long been the main backbone that Internet of Things (IoT) devices rely on to accommodate their storage and analytical needs. However, the fact that cloud systems are often located quite far from the IoT devices and the emergence of delay-critical IoT applications urged the need for extending the cloud architecture to support delay-critical services. Given that fog nodes possess low resource capabilities compared to the cloud, matching the IoT services to appropriate fog nodes while guaranteeing minimal delay for IoT services and efficient resource utilization on fog nodes becomes quite challenging. In this context, the main limitation of existing approaches is addressing the scheduling problem from one side perspective, i.e., either fog nodes or IoT devices. To address this problem, we propose in this paper a multi-criteria intelligent IoT-Fog scheduling approach using game theory. Our solution consists of designing (1) preference functions for the IoT and fog layers to enable them to rank each other based on several criteria latency and resource utilization and (2) centralized and distributed intelligent scheduling algorithms that capitalize on matching theory and consider the preferences of both parties. Simulation results reveal that our solution outperforms the two common Min-Min and Max-Min scheduling approaches in terms of IoT services execution makespan and fog nodes resource consolidation efficiency.

中文翻译：

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FoGMatch：使用博弈论的智能多标准物联网-雾调度方法

长期以来，云计算一直是物联网（IoT）设备用来满足其存储和分析需求的主要骨干。但是，云系统通常位于距离IoT设备很远的地方，而延迟关键的IoT应用程序的出现促使人们需要扩展云体系结构以支持延迟关键的服务。鉴于雾节点与云相比具有较低的资源功能，将IoT服务与适当的雾节点匹配，同时确保IoT服务的最小延迟和雾节点上的有效资源利用变得非常具有挑战性。在这种情况下，现有方法的主要局限性是从一个角度解决调度问题，即雾节点或物联网设备。为了解决这个问题，我们在本文中提出了一种使用博弈论的多标准智能物联网-雾调度方法。我们的解决方案包括设计（1）物联网和雾层的首选项功能，以使它们能够基于几种标准延迟和资源利用率彼此排名；（2）利用匹配理论并考虑首选项的集中式和分布式智能调度算法双方。仿真结果表明，我们的解决方案在物联网服务执行makepan和雾节点的资源整合效率方面优于两种常见的Min-Min和Max-Min调度方法。我们的解决方案包括设计（1）物联网和雾层的首选项功能，以使它们能够基于几种标准延迟和资源利用率彼此排名；（2）利用匹配理论并考虑首选项的集中式和分布式智能调度算法双方。仿真结果表明，我们的解决方案在物联网服务执行makepan和雾节点的资源整合效率方面优于两种常见的Min-Min和Max-Min调度方法。我们的解决方案包括设计（1）物联网和雾层的首选项功能，以使它们能够基于几种标准延迟和资源利用率彼此排名；（2）利用匹配理论并考虑首选项的集中式和分布式智能调度算法双方。仿真结果表明，我们的解决方案在物联网服务执行makepan和雾节点的资源整合效率方面优于两种常见的Min-Min和Max-Min调度方法。