Understanding Traffic Load in Software Defined WiFi Networks for Healthcare

Manzoor, Sohaib; Zhang, Chengwei; Hei, Xiaojun; Cheng, Wenqing

doi:10.1109/icce-tw46550.2019.8991777

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…Manzoor et al proposed a suitable wireless load definition and analyzed the performance of SD-WiFi by varying the load conditions [22]. Additionally, they also introduced a QoS-aware load balancing strategy (QALB) for softwaredefined wireless networks (SD-WiFi) as a solution to address the Wi-Fi congestion among OpenFlow-enabled access points (OAPs) [23].…”

Section: Related Workmentioning

confidence: 99%

K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

Gao,

Xie,

Guo

2023

IEEE Access

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Kubernetes is an orchestration platform designed for containerized applications,allows the application provider to scale automatically to match the flfluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures.However, after running for a long time, Kubernetes clusters often suffer from uneven system load, leading to a performance decline. To address this issue, a load balancing strategy called K-TAHP, based on TOPSIS and AHP , is proposed. This strategy takes cpu, memory, and bandwidth usage as load factors and uses K-TAHP to construct load evaluation. By employing a warning module and a migration module, it migrates high-load pods from overloaded nodes to nodes with lower loads, thus improving load balancing in the Kubernetes cluster and resolving performance degradation caused by load imbalance after prolonged cluster operation. Experimental results demonstrate that the K-TAHP load balancing strategy effectively enhances load balancing in Kubernetes clusters, addresses load imbalance issues after long-term operation, and ensures uninterrupted pod services during migration, thereby maintaining cluster performance.

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Section: Related Workmentioning

confidence: 99%

K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

Gao,

Xie,

Guo

2023

IEEE Access

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“…The maximum load limit is not discussed and the number of wireless stations associated to a certain AP are not sufficient to reveal the exact load values. Load in SD-WiFi is defined and wireless traffic is prioritized for an healthcare scenario in [48], [49]. Packet generation times and packet payloads are used to tune load for minimum contention in wireless transmission.…”

Section: Load Balancing In Sd-wi-fimentioning

confidence: 99%

Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

et al. 2020

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High density Wi-Fi networks require load balancing in order to ensure quality of service (QoS). In the traditional Wi-Fi networks, the wireless stations learn the access points (APs) load and make the association decisions themselves leading to uneven load distribution among the APs. The new paradigm, software defined networking (SDN), has a centralized architecture, which allows to manage, measure and control high density Wi-Fi networks easily. In this paper we propose a QoS-aware load balancing strategy (QALB) for software defined Wi-Fi networks (SD-Wi-Fi), as a solution to address the problem of Wi-Fi congestion among the OpenFlow enabled APs (OAPs). The SDN controller selects a load level up to which the association decisions are made by the OAPs without consulting the controller. The wireless stations from an overloaded OAP are handed to an underloaded OAP by considering multi-metrics such as the packet loss rate, received signal strength indicator (RSSI) and throughput. An emulation platform and a large-scalelow-cost testbed with the same settings are constructed to evaluate the performance of our load balancing strategy. The results show that in comparison to four non-static schemes such as, channel measurement based access selection scheme (CMAS), (DL-SINR) downlink-signal to interference plus noise ratio AP selection scheme (DASA), mean probe delay scheme (MPD) and RSSI scheme, the proposed QALB, optimizes the throughput up to 16%, reduces the average frame delay up to 19%, minimizes the number of re-transmissions by 49%, reduces the number of handoffs by 15%, improves the degree of load balancing by 22% and minimizes the re-association times by 38%, in high density SD-Wi-Fi. INDEX TERMS Emulation, high density, load balancing, QoS, Wi-Fi, SDN, testbed.

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“…Spontaneous deployment of the APs lead to unbalanced network resource utilization and variable AP densities [3]. Research shows that almost 70 percent of the data traffic in the future will be dependent on the Wi-Fi networks outnumbering the wired networks due to rapid increase in portable Wi-Fi associated devices [4]. High usage of smart wearables that are connected to wireless smart phones through Wi-Fi networks instead of cellular connections are making outdoor usage of Wi-Fi networks inevitable [5].…”

Section: Introductionmentioning

confidence: 99%

Achieving QoS in Smart Cities Using Software Defined Wi-Fi Networks

Manzoor,

Ratyal,

Mohamed

2023

IEEE Access

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The execution of smart cities around the globe has risen due to the steady connectivity and increased number of wireless devices. Due to low-cost network construction and simple technical implementation, Wi-Fi networks have become a dominant wireless technology to enable the connectivity of Internet-of-Things (IoT) in smart cities. There are a number of services and applications running in smart cities with different demands of the quality of service (QoS). The paper focuses to address the latency problem, which is a key performance metric regarding QoS, in time sensitive applications in smart cities. The emerging paradigm, Software-defined Networking (SDN) is extended for Wi-Fi networks to ensure fairness of traffic load among the access points (AP). We propose three algorithms based on service time, M/G/1 analysis and AP selection to determine the packet transmission delay, packet latency rates and choosing a least loaded destination AP respectively. The optimization of load among the APs ensures a reduced packet latency factor, when a communication link is formed between the smart city IoT devices and the APs. A symmetric load index and a reduced packet latency rate is maintained between the IoT devices and the OpenFlow enabled APs using three software-defined algorithms designed in this study. A Linux based software-defined testbed is developed to ensure the credibility of the algorithms developed. Extensive experimentation using the hardware devices confirm that the proposed algorithms are efficient enough to reduce the latency rate and enhance the throughput rate by 17%, 13% and 9% when compared to received signal strength indicator scheme (RSSI), Po-Fi scheme and aggregated Wi-Fi scheme respectively, by shifting the wireless traffic load from a higher packet latency IoT device to a least loaded AP.

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Understanding Traffic Load in Software Defined WiFi Networks for Healthcare

Cited by 11 publications

References 7 publications

K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP

Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

Achieving QoS in Smart Cities Using Software Defined Wi-Fi Networks

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