Deep Learning-Based Early Detection and Avoidance of Traffic Congestion in Software-Defined Networks

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dc.contributor.authorPrabhavat, Sumet
dc.contributor.authorThongthavorn, Thananop
dc.contributor.authorPasupa, Kitsuchart
dc.date.accessioned2026-08-06T10:34:43Z
dc.date.available2026-08-06T10:34:43Z
dc.date.issued2022-01-01
dc.description.abstractSoftware-defined Networking (SDN) provides an easy way to monitor network and traffic conditions by employing software-based controllers to communicate with the hardware directly. It provides helpful information that enables efficient routing decisions. This research study attempted to use deep learning techniques - Long Short-term Memory, Bidirectional Long Short-term Memory, and Gated Recurrent Unit - to predict network traffic to allow the controller to early detect congestion. The traffic flow in a network link that will likely be congested will be rerouted to a new path with the largest available bandwidth. Various scenarios were simulated to evaluate our deep learning-based SDN controller (Ryu controller platform). The results show that our proposed deep learning-based SDN controller outperformed the traditional load balancing technique.
dc.identifier.citationIcitee 2022 Proceedings of the 14th International Conference on Information Technology and Electrical Engineering, 1-6, 2022
dc.identifier.doi10.1109/ICITEE56407.2022.9954107
dc.identifier.other2-s2.0-85143618167
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12598
dc.sourceIcitee 2022 Proceedings of the 14th International Conference on Information Technology and Electrical Engineering
dc.subjectCongestion control
dc.subjectDeep learning
dc.subjectSoftware-defined network
dc.subjectTraffic engineering
dc.titleDeep Learning-Based Early Detection and Avoidance of Traffic Congestion in Software-Defined Networks
dc.typeConference Paper

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