Self-Adaptive Management of SDN Distributed Controllers for Highly Dynamic IoT Networks

Abstract

The Internet of Things (IoT) is about connecting dynamically billion of devices to the Internet. This large-scale and dynamic topology is very challenging for IoT deployment and management. Software-Defined Networking (SDN) has been applied more and more in recent years as a solution for IoT challenges. The SDN concept of decoupling the control plane from the data plane promotes logically centralized visibility of the entire network and enables the applications to innovate through network programmability. At the same time, there are still some open issues, such as scalability in large IoT environments that include several devices. To face scalability challenges, SDN proposes distributed controllers as a solution to decentralize the control plane while maintaining the logically centralized network view. However, SDN-based architecture, that provides the flexibility and scalability, still lacks the smart or intelligent management to self-adapt to possible dynamic network topology changes. To over-come such issues, we propose a framework that answers automatically the business demands and makes the network self-adaptive. The topology deployment decision is made based on information that the controller gives. So for making sure that our proposed framework gives the best results, we have to study first the topology discovery mechanism in a distributed controller. In this paper, we introduce a self-adaptive management framework of SDN controllers for highly dynamic IoT networks. We evaluate performances of the two most popular distributed SDN controllers (i.e. ONOS and ODL) in a realistic scenario where the network topology changes dynamically. Results show the outperforming of ONOS compared to ODL in discovering the highly dynamic IoT network.