Performance analysis of fair scheduler for A-MSDU aggregation in IEEE802.11n wireless networks

Maqhat, Bakeel; Baba, Mohd Dani; Rahman, Ruhani Ab; Saif, Anwar

doi:10.1109/iceese.2014.7154608

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…Most work concentrated on scheduling schemes incorporating the length of the traffic queues, the time to serve a packet, or the time waiting on the scheduler. Later, with the improvements in radio resource utilization provided by the IEEE 802.11n amendment [28], researchers focused on channel optimization and fairness (e.g., modifying or predicting the Aggregated MAC Service Data Unit (A-MSDU) behavior) [29][30][31][32][33]. However, such proposals required modifications to the driver (e.g., frame formats) and are no longer compliant with the standard.…”

Section: Resource Allocation and Qos Supportmentioning

confidence: 99%

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

Isolani

Kulenkamp

Márquez-Barja

et al. 2021

Sensors

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With the emergence of 5G networks and the stringent Quality of Service (QoS) requirements of Mission-Critical Applications (MCAs), co-existing networks are expected to deliver higher-speed connections, enhanced reliability, and lower latency. IEEE 802.11 networks, which co-exist with 5G, continue to be the access choice for indoor networks. However, traditional IEEE 802.11 networks lack sufficient reliability and they have non-deterministic latency. To dynamically control resources in IEEE 802.11 networks, in this paper we propose a delay-aware approach for Medium Access Control (MAC) management via airtime-based network slicing and traffic shaping, as well as user association while using Multi-Criteria Decision Analysis (MCDA). To fulfill the QoS requirements, we use Software-Defined Networking (SDN) for airtime-based network slicing and seamless handovers at the Software-Defined Radio Access Network (SD-RAN), while traffic shaping is done at the Stations (STAs). In addition to throughput, channel utilization, and signal strength, our approach monitors the queueing delay at the Access Points (APs) and uses it for centralized network management. We evaluate our approach in a testbed composed of APs controlled by SD-RAN and SDN controllers, with STAs under different workload combinations. Our results show that, in addition to load balancing flows across APs, our approach avoids the ping-pong effect while enhancing the QoS delivery at runtime. Under varying traffic demands, our approach maintains the queueing delay requirements of 5 ms for most of the experiment run, hence drawing closer to MCA requirements.

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Section: Resource Allocation and Qos Supportmentioning

confidence: 99%

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

Isolani

Kulenkamp

Márquez-Barja

et al. 2021

Sensors

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“…Control bits are modified for each sub-frame to facilitate swifter retransmissions. This work was later implemented using NS-2 in [28]. Similarly, the authors of [29] seek improvements for error-prone channels by adding control bits to every subframe with the aim of enabling per-subframe retransmissions.…”

Section: Solutions For Frame Length Optimizationmentioning

confidence: 99%

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

2020

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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.

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“…In their proposal, control bits are separately adjusted for each sub-frame to enable faster retransmissions. This work is later implemented using NS-2 [14]. Similarly, in [15] the authors pursue improvements for error-prone channels by adding control bits to every subframe to enable per-subframe retransmissions.…”

Section: B Frame Length Selection In Wlansmentioning

confidence: 99%

aiOS: An Intelligence Layer for SD-WLANs

Coronado

Thomas

Bayhan

et al. 2020

NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium

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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.

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Performance analysis of fair scheduler for A-MSDU aggregation in IEEE802.11n wireless networks

Cited by 11 publications

References 22 publications

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

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

aiOS: An Intelligence Layer for SD-WLANs

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