Congestion Control Proposal in SDN With Random Early Detection

• Published in: 2020 IEEE Congreso Bienal de Argentina (ARGENCON) pp. 1 - 5
• Main Authors: Airaldi, Luis Lezcano, Scappini, Reinaldo J. R., Gramajo, Sergio, Bolatti, Diego
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2020
• Subjects: Adaptation models; Congestion Control; Data models; Neural networks; Performance evaluation; Predictive models; Protocols; QoS; Quality of service; RED; SDN
• DOI: 10.1109/ARGENCON49523.2020.9505361

The emerging technology of SDN (Software Defined Networks) separates the data control plane from the forwarding plane while maintaining a centralized control of the network management. The SDN features require new traffic engineering techniques that exploit the global (centralized) network view, and the status and features of traffic flows. Our purpose is to perform traffic engineering in an SDN architecture, using the OpenFlow protocol capabilities and the potential of the SDN controller to collect operational data of the entire network, such as topology, latency, buffer utilization and frame sizes of the controlled devices in order to implement QoS. Considering that the performance of the network is an essential component of Quality of Service (QoS), and congestion is the main factor that affects it, this paper explores a method to search for alternative paths based on data from switches using the Random Early Detection (RED) congestion control mechanism.