A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices

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.