QoE-Centric Adaptive Multipath Routing under R-Factor with Load Balancing in SDN

Lemeshko, Oleksandr; Yevdokymenko, Maryna; Yeremenko, Oleksandra; Al-Dulaimi, Aymen Mohammed Khodayer; Segeč, Pavel; Papán, Jozef

doi:10.1109/picst51311.2020.9467903

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…For example, Nightingale et al [18] have proposed a 5G-QoE framework to address the QoE modeling for UHD video flows in 5G networks. Lemeshko et al [3] developed and researched a model of adaptive routing with the provision of QoE. In their work, the application's QoE is calculated as an R-factor from the E-model.…”

Section: B Related Workmentioning

confidence: 99%

“…A significant benefit of this approach is that testing is initiated only when a specific path is requested, ensuring that the assessed path precisely mirrors the anticipated available resources. E2E metrics have gained traction in QoS routing [24], [25] as well as QoE routing [2], [3], [22] to represent the network state. E2E test, faces challenges as well.…”

Section: B Related Workmentioning

confidence: 99%

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QoE-Oriented Routing Mixing Application KPIs and Link Metrics Through Machine Learning

Wang,

Wu,

Delaney

2024

IEEE Access

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As the complexity of network end devices and applications grows, network managers face increasing difficulty in meeting specific end user requirements, leading to reduced user experience and inefficient resource management. This paper introduces a Quality of Experience (QoE)-oriented routing strategy to enhance user experience by selecting routing paths based on application-specific QoE.Application key performance indicators (KPIs) and dynamic link metrics are utilized to represent real-time QoE and network state. This data builds QoE models for various applications such as video streaming, VoIP, and web map, using four learning methods. The trained models are implemented in a software-defined networking (SDN) controller for optimal QoE routing. Evaluations using the Mininet network simulator reveal that the proposed QoE routing strategy can select the best path 78.4% of the time which is almost 20% more than the top-performing state-of-the-art. This results in measurably higher application performance, proving the efficiency of the proposed approach in improving the application's QoE.INDEX TERMS link metrics, machine learning, Quality of experience (QoE), routing, software-defined networking (SDN).

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

QoE-Oriented Routing Mixing Application KPIs and Link Metrics Through Machine Learning

Wang,

Wu,

Delaney

2024

IEEE Access

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“…From a simulation point of view, applying a nonuniform distribution of traffic would lead to more links loaded. On the other hand, from a practical point of view, multipath routing would lead to a more uniform traffic distribution over links to load more of them [23][24][25]. Analysis of results lets to see that there is some traffic quota 𝑞 𝑝 which changes how a given QoS parameter varies in function of q parameter.…”

Section: 𝐿(𝑐 𝑖mentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Kaczmarek

Wolff

2021

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The paper contains a description and research results of the proposal for distributed QoS extensions for Flow-Based Networking. These QoS extensions let the network accept or reject flows based on current network load and QoS promises for each of the flows. Proposed solution consists of two distributed components, each of them performing in every node, measurement system and access control. The solution could be applied in any network architecture that is able to distinguish flows and routers in this architecture contains flow state table. Proposed approach was verified by simulation, in FSA architecture. Verification was done for six different network structures servicing two traffic classes (MRS, ARS). The results of the simulation tests have confirmed that the average time delay and packet loss ratio in the network with proposed extensions are below thresholds and meet the requirements recommended by ITU-T.

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Research of the QoE Fast ReRouting Processes with Differentiated R-Factor Maximization for VoIP-Flows Using the Tensor Model of the Corporate Telecommunication Network

Lemeshko

Yeremenko

Yevdokymenko

et al. 2021

Current Trends in Communication and Information Technologies

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QoE-Centric Adaptive Multipath Routing under R-Factor with Load Balancing in SDN

Cited by 5 publications

References 22 publications

QoE-Oriented Routing Mixing Application KPIs and Link Metrics Through Machine Learning

QoE-Oriented Routing Mixing Application KPIs and Link Metrics Through Machine Learning

International Journal of Electronics and Telecommunications

Research of the QoE Fast ReRouting Processes with Differentiated R-Factor Maximization for VoIP-Flows Using the Tensor Model of the Corporate Telecommunication Network

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