An Efficient Multipriority Data Packet Traffic Scheduling Approach for Fog of Things

With the proliferation of heterogeneous services in scalable Internet-of-Things (IoT) architectures, the Cloud of Things begins to face latency-based network challenges. As a solution to these problems, the Fog-of-Things (FoT) paradigm has emerged. This paradigm promises numerous benefits for latency-sensitive IoT service design, by reducing IoT data traffic toward the cloud. Since FoT utilizes heterogeneous services, the decision outputs of these services are transmitted by multipriority data packet traffic patterns. For the full utilization of fog computing benefits, we need to model this traffic and apply an efficient data traffic scheduling approach. In this study, we address the starvation challenge of multipriority FoT data traffic scheduling in a scalable fog architecture and propose a complex event processing-based efficient scheduling policy for its prevention. We first give the arrival-service model for FoT data traffic using finite-size multilevel waiting lines. Then, we compare the proposed policy with the first-in-first-out and multipriority-discipline queue policies through a comprehensive analysis of waiting times and wait-time gap characteristics. We have also conducted extensive simulation tests to explore the performance of these policies in our testbed for up to 800 clients communicating with the fog system. The results reveal that the empirical values obtained from the tests verify the theoretical model and the proposed approach can successfully relieve the waiting-time gaps observed in priority levels.