Implementing an SDN based learning switch to measure and evaluate UDP traffic

The traditional architecture of the network has legacy issues such as manageability, scala- bility and flexibility. Network administrators do not have much power to control and mon- itor their networks. Therefore, a new network architecture, so-called software-defined net- working (SDN), has emerged to address these challenging issues by redefining the network to make it programmable. SDN relies on decoupling the control plane from the data plane so that it is central and programmable. OpenFlow protocol, which is an open source proto- col adopted by the Open Networking Foundation, is empowered to facilitate the communi- cation between controller and switch and to enable agile management of the network. However, OpenFlow-based SDN has two shortcomings that affect the network’s perfor- mance. First, the communication between the controller and the switch introduces further delay in round-trip time (RTT). Second, the RTT delay results in packet loss or packets ar- riving at the destination out of order. These two major issues are more likely to occur with UDP traffic due to its characteristics. This paper explains the reasons behind these issues, hypothetically discusses some potential solutions and implements the proposed algorithm. The proposed algorithm reduces the number of packets out of order by increasing the hard timeout time of flow entries when the CPU’s usage increases and by gathering statis- tics from the switch more often. During the demonstration of OpenFlow-based SDN, a POX controller and Mininet are used to emulate the infrastructure needed. Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Electrical Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Electrical Engineering, [(in press), (2017)] DOI: 10.1016/j.compeleceng.2017.07.002 © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/