With the increasing complexity of networks, Software Defined Networks have been developed to logically control all networking devices such as switches and bridges centrally through the use of software defined network controllers. Controllers include beacon, opendaylight, floodlight, pox, ryu and many more.
Ryu SDN controller is one of the most popular python developed controller. Just as many other controllers’ Ryu may become a bottleneck since all other network controller controls network devices. Desired performance in any network cannot be achieved if a controller is used without any guidance. Therefore, ensuring proper usage of a controller by knowing under what circumstances ryu controller performs efficiently prior to deployment of the network is an important course. SDN network topologies affect the performance of a controller basing of the complexity of the topology and type of the topology.
This study aimed on investigating performance of Ryu software defined network controller on different network topologies. Ryu controller performance was evaluated by using throughput and latency performance metrics and openflow 1.3 was used as a southbound protocol. Throughput was measured with the help of iperf tool and latency was measured using ping command to determine the round trip time. Performances of Ryu in tree, ring, mesh, torus and hypercube topologies were measured and comparison was done. Significance of results obtained was also determined to see to what extent do network topology affect the performance of Ryu SDN controller.
Results shows that default network topologies had a better performance than custom network topologies with the highest average throughput recorded of 9.57Gbits/sec on torus topology of 9 switches and lowest average latency recorded was 0.077ms on mesh topology. Furthermore, the results show that except for mesh topology all other topologies can give a desired performance in terms of latency at a scalability of up to 64 switches. Finally, a framework that guides the use of Ryu controller on different network topologies was developed with the help of the results obtained after the performance evaluation.
SSA, R (2021). Investigatingthe Performance Of Ryu Software Defined Network Controller On Different Network Topologies. Afribary.com: Retrieved May 13, 2021, from https://afribary.com/works/investigatingthe-performance-of-ryu-software-defined-network-controller-on-different-network-topologies
Research, SSA. "Investigatingthe Performance Of Ryu Software Defined Network Controller On Different Network Topologies" Afribary.com. Afribary.com, 26 Apr. 2021, https://afribary.com/works/investigatingthe-performance-of-ryu-software-defined-network-controller-on-different-network-topologies . Accessed 13 May. 2021.
Research, SSA. "Investigatingthe Performance Of Ryu Software Defined Network Controller On Different Network Topologies". Afribary.com, Afribary.com, 26 Apr. 2021. Web. 13 May. 2021. < https://afribary.com/works/investigatingthe-performance-of-ryu-software-defined-network-controller-on-different-network-topologies >.
Research, SSA. "Investigatingthe Performance Of Ryu Software Defined Network Controller On Different Network Topologies" Afribary.com (2021). Accessed May 13, 2021. https://afribary.com/works/investigatingthe-performance-of-ryu-software-defined-network-controller-on-different-network-topologies