Current 802.11-based WLANs are asked to support an ever increasing number of services and applications, each of them characterized by a diverse set of requirements in terms of bitrate, latency, and reliability. Network virtualization and programmability are two emerging trends that can support the realization of such a vision in a cost-effective fashion. In this paper we introduce Lasagna, a novel end-to-end solution that enables flexible management of slices encompassing both the wired and the wireless segments of an Enterprise WLAN. Lasagna allows flexible management of network slices to meet their respective service requirements. An experimental evaluation carried out over a real-world testbed shows that Lasagna can ensure both functional and performance isolation between the different slices and efficient radio resource utilization. We release the entire implementation including the controller and the datapath under a permissive license for academic use.

Lasagna: Programming Abstractions for End-to-End Slicing in Software-Defined WLANs

The ever-increasing deployment of new wireless technologies and the demand for mobile services delivery has hampered the efficient and reliable wireless communication. While traditional IEEE 802.11 Wireless Local Area Networks (WLANs) suffer interference from other wireless technologies, their manageability is currently limited. In this context, the overall performance in terms of bitrate, latency, and reliability depends on a number and often dynamically changing aspects where the Medium Access Control (MAC) layer plays a crucial role. Current IEEE 802.11 MAC protocols cannot be programmed fine-grained enough and they cannot manage multiple networks at runtime. In this paper, we propose an approach and an algorithm for on-the-fly End-to-End (E2E) Quality of Service (QoS) slice orchestration and IEEE 802.11 MAC management based on Software-Defined Networking (SDN) principles. We argue that, by performing slice orchestration and IEEE 802.11 MAC management at runtime, it is possible to deliver improved and reliable E2E QoS. To demonstrate the feasibility of our approach, we developed a prototype where our hands-on experiments show that we can reduce the average latency in half while compromising only less than 8% of the average throughput.

SDN-based Slice Orchestration and MAC Management for QoS delivery in IEEE 802.11 Networks

Code dissemination is a main component of reprogramming which enables over-the-air software update in wireless sensor networks (WSNs). In this paper, we present an adaptive code dissemination based on link quality (ACODI), which aims to minimize energy consumption. Compared to prior works on code dissemination, ACODI has a variety of notable features. First, it dynamically adapts the payload size in terms of energy efficiency. Second, it provides very low-overhead link estimation method. Finally, it is gracefully integrated into Deluge, which is the de facto standard code dissemination protocol in WSNs, and implemented on the TinyOS platform with very small overhead in terms of computation and memory. Our experiments using TelosB motes in the indoor testbed show that ACODI outperforms Deluge-22 and Deluge-108, which are Deluge with a fixed payload size of 22 and 108 bytes, respectively, in terms of energy efficiency and completion time.

Adaptive Code Dissemination Based on Link Quality in Wireless Sensor Networks

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.

/pdf/adaptive-ml-based-frame-length-optimisation-in-enterprise-sd-1i3r28xes5.pdf

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

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

The IEEE 802.11a can provide up to 54Mbps data rate and the IEEE 802.11n provide much higher data rates by the supporting PHY layer technique such as MIMO (Multiple Input Multiple Output). However, the medium access control of IEEE 802.11 WLANs were designed for low data rate such as 2Mbps. So, in high data rate WLANs, IEEE 802.11 WLANs are not efficient. Due to fixed overhead (control frames, preamble, and PLCP header are transmitted low data rate and IFS time is fixed irrespective of transmission rate), increasing of data transmission rate is not easy. To reduce fixed transmission overhead, frame aggregation has been proposed. However, in a noisy channel environment, frame aggregation leads to longer frame and it may be provoked transmission errors. In this paper, we propose the Dynamic Frame Aggregation (DFA) scheme. The DFA can aggregate frames according to channel condition properly. And the DFA works based on optimal pair of transmission rate and transmission frame length. So the DFA can avoid transmission errors and reduce transmission overhead simultaneously. The performance of the proposed scheme is evaluated by analytic modeling. The results show that the DFA provides better throughput than the frame aggregation scheme.

Dongwan Kim

Papers

Throughput enhancement by Dynamic Frame Aggregation in multi-rate WLANs