Evaluating Scalability, Resiliency, and Load Balancing in Software-Defined Networking

Abstract

With emerging technologies like cloud computing and big data, managing traditional networks has become more demanding. Software-defined networking (SDN) promises faster implementation, flexibility, and simplified network management. However, due to SDN’s centralized nature, it encounters limitations. SDN controllers should have enough processing power to deal with a high amount of flow. In addition, a single point of failure may affect the network’s resiliency. For these issues, multi-instance implementation enables distributed control. However, this solution implies an intrinsic controller-to-controller synchronization channel. In this article, we propose different failure scenarios in both the data and control planes to provide network administrators with a clear view of the constraints of network reliability, load balancing, and scalability in SDN environments. The simulation results show that, regarding resiliency, SDN networks require half the time compared to traditional networks in order to recover from a link failure. Regarding load-balancing capabilities, load balancing is not guaranteed with the reactive forwarding approach (on-demand flow installation). Lastly, the SDN multi-instance solution impacts the network performance by between 1% and 21% compared to the single-instance case.

Keywords

• software-defined networking
• network evaluation
• ONOS
• ODL