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 ML-based 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.
Highlights
Progress in the communications industry has generally been marked by hardware and computation-centric innovations
It is becoming increasingly evident that there is a need to enhance the flexibility of the resource provisioning, and of the physical infrastructure. This involves a change from reactive to proactive and automated network management, in which the analytics made available at the Software-Defined Networking (SDN) controller lay the basis for knowledge-based and self-driven networks in what is known as Knowledge-Defined Networking (KDN) [3]
To assess the solution proposed in this work, we considered an IEEE 802.11 enterprise Wireless Local Area Networks (WLANs), which was modelled via simulation with the aim of generating diverse channel conditions in a controlled environment
Summary
Progress in the communications industry has generally been marked by hardware and computation-centric innovations. 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], demonstrating that traditional approaches to network management are no longer adequate. It is becoming increasingly evident that there is a need to enhance the flexibility of the resource provisioning, and of the physical infrastructure This involves a change from reactive to proactive and automated network management, in which the analytics made available at the SDN controller lay the basis for knowledge-based and self-driven networks in what is known as Knowledge-Defined Networking (KDN) [3]. This paradigm aims at building smarter networks able to autonomously optimize operation and management by extending the SDN architecture with a Knowledge Plane (KP), a new component characterized by the active inclusion of Machine Learning (ML) techniques
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