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.

aiOS: An Intelligence Layer for SD-WLANs / Coronado, Estefania; Thomas, Abin; Bayhan, Suzan; Riggio, Roberto. - (2020). (Intervento presentato al convegno NOMS 2020 tenutosi a Budapest, Hungary nel 20-24 April 2020) [10.1109/NOMS47738.2020.9110260].

aiOS: An Intelligence Layer for SD-WLANs

Thomas, Abin;Riggio, Roberto
2020-01-01

Abstract

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.
2020
NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium
Piscataway, NJ
IEEE
9781728149738
Coronado, Estefania; Thomas, Abin; Bayhan, Suzan; Riggio, Roberto
aiOS: An Intelligence Layer for SD-WLANs / Coronado, Estefania; Thomas, Abin; Bayhan, Suzan; Riggio, Roberto. - (2020). (Intervento presentato al convegno NOMS 2020 tenutosi a Budapest, Hungary nel 20-24 April 2020) [10.1109/NOMS47738.2020.9110260].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/276305
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