Machine Learning-Based Prediction Models for Control Traffic in SDN Systems

Yeonho Yoo, Gyeongsik Yang, Changyong Shin, Junseok Lee, Chuck Yoo

Research output: Contribution to journalArticlepeer-review


This article presents Elixir, an automated prediction model formulation framework for control traffic using machine learning. Control traffic is vital in software-defined networking (SDN) systems because it determines the reliability and scalability of the entire system. Various studies have sought to design control traffic prediction models for the proper provisioning and planning of SDN systems. However, previously proposed models are based on descriptive modeling, well-suited for only specific SDN system instances. Furthermore, these models exhibit poor accuracy (errors of up to 85%) because of the heterogeneity of SDN systems. Because descriptive modeling requires a significant amount of human contemplation, it is impossible to formulate adequate prediction models for countless SDN system instances. Elixir addresses this problem by applying machine learning. Elixir starts the model formulation through self-generated datasets. Then, Elixir searches prediction models to fit the accuracy for respective SDN systems. Also, Elixir picks robust models that exhibit reasonable accuracy even in a network topology that differs from the topology used for model training. We evaluate the Elixir framework on nine heterogeneous SDN systems. As a key outcome, Elixir significantly reduces prediction errors, achieving up to 10.6× improvement compared to the previous model for control traffic throughput of OpenDayLight controller.

Original languageEnglish
Pages (from-to)4389-4403
Number of pages15
JournalIEEE Transactions on Services Computing
Issue number6
Publication statusPublished - 2023 Nov 1

Bibliographical note

Publisher Copyright:
© 2008-2012 IEEE.


  • control traffic
  • Machine learning
  • prediction model formulation
  • prediction robustness
  • software-defined networking

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems and Management


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