Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs
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Adaptive ML‑Based Frame Length Optimisation in Enterprise SD‑WLANs Estefanía Coronado1 · Abin Thomas1 · Roberto Riggio1 Received: 10 January 2020 / Revised: 2 March 2020 / Accepted: 10 March 2020 © The Author(s) 2020
Abstract Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive MLbased approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms. Keywords Machine learning · Supervised learning · WLANs · IEEE 802.11 · Frame length optimization · Aggregation · SDN · Network management
* Estefanía Coronado [email protected] Abin Thomas [email protected] Roberto Riggio [email protected] 1
Smart Networks and Services (SENSE), Fondazione Bruno Kessler, Trento, Italy
13
Vol.:(0123456789)
Journal of Network and Systems Management
1 Introduction Progress in the communications industry has generally been marked by hardware and computation-centric innovations. However, networking systems have gradually begun to evolve dynamically towards service-oriented architectures that break the chains of an outmoded dependence on monolithic network stacks and conventional hardware advancements. This change has been the objective pursued by Software-Defined Networking (SDN), which introduced a management architecture characterized by the decoupling of the control and data planes across various degrees of centralisation [1, 2], thus demonstrating that traditional approaches to network management are no longer adequate. This is especially true when it comes to wireless networks, where the level of complexity, which is due to their error-prone nature, longer delays, and the inefficient and inflexible use of resources, requires the use of advanced network management policies. SDN marks a turning point in networking, giving rise to a new generation of programmable and ser
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