OFDPv2 : an efficient protocol for topology discovery in openflow-based software defined networking

Software Defined Networking (SDN) is a new networking pattern, with great possibilities to increase network efficiency, ease the complexity of network control and management, and speed up the degree of technological novelty. Earliest, one of the core concepts of SDN is the separation of the network’s control and data plane. The intellect and the control of the network operation and management, such as routing, are removed from the forwarding devices and are concentrated in a logically centralized component, called the SDN controller. In order for the controller to configure and manage the network, it needs to have up-to-date information about the network state, more especially its topology. Thus, topology discovery is a thoughtful section of any Software Defined Network Architecture. In this project, we evaluate the efficiency of OFDP, the current de facto standard approach implemented by the major SDN controller frameworks, and proposes an improved version with simple and practical modifications, which achieve a significantly improved efficiency and reduced control overhead. We have implemented our new topology discovery approach on the widely used POX controller platform, and have evaluated it for a range of network topologies through experiments using the Mininet network emulator. Our results show that our proposed modifications achieve a significantly reduced the number of control messages and increase efficiency to a range of an up to minimum67-80% maximum of the SDN controllers and switches whereas the number of LLDP Packet-In messages, the other type of topology discovery control messages, is unchanged. And also achieves bandwidth availability to a range of 50-80% over the state-of–art (OFDP)by remarkably reduced control traffic overhead on the SDN controller and also our proposed protocol is completely compatible with the Open Flow standard while delivering identical discovery functionality.

Preview Text FSKTM 2017 9 - IR.pdf Download (1MB) | Preview

Actions (login required)

View Item