Differential flow space allocation scheme in SDN based fog computing for IoT applications

Abstract

Technological advancements in wireless communications and electronics have enabled the rapid evolution of smart things connected to the Internet. Traditional networks face the challenges of scalability, real-time data delivery, programmability and mobility to support these smart objects collectively known as Internet of Things (IoT). To solve these issues, the integration of two emerging network technologies, namely, software defined networking (SDN) and Fog computing have gained a momentum as a novel model that support IoT architecture for manageability and low latency. SDN has a logically centralized network control plane, which is used for implementing sophisticated mechanisms of traffic control and resource management. On the other hand, Fog computing enables IoT devices’ data to be processed and managed at the network edge, thus providing support for applications that require very low and predictable latency. Though the communication latency is substantially reduced by the adoption of distributed fog layer closer to IoT ends, the latency overhead in the IoT/fog network is not only because of long distance between IoT devices and the cloud, but it is also caused by flow entry installation delay, which comes from limitations in data and control space designs. Traditional fog networks lack priority based fine-grain control over allocation of flows, and this incurs unnecessary delay for critical packets. The impact of packet blocking on QoS delivery could be reduced if the programmability power of SDN approach is employed in IoT applications for priority oriented flow space management in fog networks. In this paper, we propose a converged SDN and IoT/fog architecture which employs differential flow space allocation for heterogeneous IoT applications per flow classes to satisfy priority based quality of service requirements. Our analytical results demonstrate that urgent flow classes are served more efficiently than Naive approach without compromising fairness of allocation for normal flow classes.