The emergence of the Internet of Things (IoT) paradigm has led to the rise of a variety of applications with different characteristics and Quality of Service (QoS) requirements. Those applications require computational power and have time sensitive requirements. Cloud computing paradigm provides an illusion to consumers with unlimited computation resource power. However, cloud computing fails to deliver on the time-sensitive requirements of applications. The main challenge in the cloud computing paradigm is the associated delays from the edge IoT device to the cloud data center and from the cloud data center back to the edge device. Fog computing extends limited computational services closer to the edge device to achieve the time sensitive requirement of applications. This work proposes a scheduling solution which adopts the three-tier fog computing architecture in order to satisfy the maximum number of requests given their deadline requirements. In this work, an optimization model using mixed integer programming is introduced to minimize deadline misses. The model is validated with an exact solution technique. The scheduling problem is known to be an NP-hard, and hence, exact optimization solutions are inadequate for a typical size problem in fog computing. Given the complex nature of the problem, a heuristic approach using the genetic algorithm (GA) is presented. The performance of the proposed GA was evaluated and compared against round robin and priority scheduling. The results show that the deadline misses of the proposed approach is 20%–55% better than the other techniques.

物联网（IoT）范式的出现导致了具有不同特征和服务质量（QoS）要求的各种应用程序的兴起。这些应用需要计算能力，并且对时间敏感。云计算范例为消费者提供了无限的计算资源能力的幻觉。但是，云计算无法满足应用程序对时间敏感的要求。云计算范例中的主要挑战是从边缘物联网设备到云数据中心以及从云数据中心到边缘设备的相关延迟。雾计算将有限的计算服务扩展到了边缘设备附近，从而满足了应用程序对时间的要求。这项工作提出了一种调度解决方案，该方案采用三层雾计算体系结构，以便在给定期限要求的情况下满足最大数量的请求。在这项工作中，引入了使用混合整数编程的优化模型以最大程度地减少截止期限。该模型已通过精确的求解技术进行了验证。已知调度问题是NP问题，因此，精确的优化解决方案不足以解决雾计算中的典型大小问题。考虑到问题的复杂性，提出了一种使用遗传算法（GA）的启发式方法。评估了拟议GA的性能，并将其与轮询和优先级调度进行了比较。结果表明，与其他技术相比，该方法的最后期限错过率提高了20％至55％。