aiOS: An Intelligence Layer for SD-WLANs

Estefanía Coronado, Abin Thomas, Suzan Bayhan, Roberto Riggio

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
3 Downloads (Pure)


Software-Defined Networking promises to deliver a more manageable network whose behaviour could be easily changed using applications written in high-level declarative languages running on top of a logically centralized control plane resulting, on the one hand, in the mushrooming of complex point solutions to very specific problems and, on the other hand, in the creation of a multitude of network configuration options. This fact is especially true for 802.11-based Software-Defined WLANs (SD-WLANs). It is our standpoint that to tame this increase in complexity, future SD-WLANs must follow an Artificial Intelligence (AI) native approach. In this paper we present aiOS, an AI-based Operating System for SD-WLANs. Then, we use aiOS to implement several Machine Learning (ML) models for user-adaptive frame length selection in SD-WLANs. An extensive performance evaluation carried out on a real-world testbed shows that this approach improves the aggregated network throughput by up to 55%. Finally, we release the entire implementation including the controller, the ML models, and the programmable data-path under a permissive license for academic use.
Original languageEnglish
Title of host publication2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020)
Subtitle of host publicationManagement in the Age of Softwarization and Artificial Intelligence
Place of PublicationPiscataway, NJ
Number of pages9
ISBN (Electronic)978-1-7281-4973-8
ISBN (Print)978-1-7281-4974-5
Publication statusPublished - 8 Jun 2020
Event17th IEEE/IFIP Network Operations and Management Symposium, NOMS 2020: Management in the Age of Softwarization and Artificial Intelligence - Virtual conference, Budapest, Hungary
Duration: 20 Apr 202024 Apr 2020
Conference number: 17 (Conference)

Publication series

NameIEEE/IFIP Network Operations and Management Symposium (NOMS)
ISSN (Print)1542-1201
ISSN (Electronic)2374-9709


Conference17th IEEE/IFIP Network Operations and Management Symposium, NOMS 2020
Abbreviated titleNOMS
Internet address


  • IEEE 802.11
  • Machine learning
  • Software-defined networking
  • WLANs
  • Aggregation
  • Frame length selection
  • 22/3 OA procedure


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