Abstract
Network slicing has recently been proposed as one of the main enablers for 5G networks. The slicing concept consists of the partition of a physical network into several self-contained logical networks (slices) that can be tailored to offer different functional or performance requirements. In the context of 5G networks, we argue that existing ubiquitous WiFi technology can be exploited to cope with new requirements. Therefore, in this paper, we propose a novel mechanism to implement network slicing in WiFi Access Points. We formulate the resource allocation problem to the different slices as a stochastic optimization problem, where each slice can have bit rate, delay, and capacity requirements. We devise a solution to the problem above using the Lyapunov drift optimization theory, and we develop a novel queuing and scheduling algorithm. We have used MATLAB and Simulink to build a prototype of the proposed solution, whose performance has been evaluated in a typical slicing scenario.
Highlights
Network Slicing is a new network paradigm developed within the context of recent 5G networks, which proposes the partition of the physical network infrastructure into multiple selfcontained logical networks called slices
The rest of the paper is organized as follows: in Section II, we review the state of the art in wireless slicing and scheduling mechanisms; in Section III we describe our system and Quality of Service (QoS) guarantees models
We proposed a dynamic resource allocation mechanism to support the development of network slicing in WiFi Access Points
Summary
Network Slicing is a new network paradigm developed within the context of recent 5G networks, which proposes the partition of the physical network infrastructure into multiple selfcontained logical (or virtual) networks called slices. We focus on performance requirements, and we elaborate on how it could be implemented in WiFi Radio Access Networks (RANs) We define this slicing strategy as Qualityof-Service Slicing (QoSS): slices supporting different services and ensuring their Quality of Service (QoS), regardless of the required resources. This method permits to obtain an equivalent deterministic problem, which provides an approximate solution We perform this by extending our previous work on airtime slicing [6], [7] and guaranteed bit rate slicing [8] in WiFi APs. The main differences of this work with [8] are threefold: 1) We add a new QoS guarantee to the system model, a delay bound to every packet of a flow.
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