Improvement of Method of Balanced Queue Management on Routers Interfaces of Telecommunication Networks

Lemeshko, Oleksandr; Lebedenko, Tetiana; Al-Dulaimi, Mohammed Khodayer Hassan

doi:10.1109/aiact.2019.8847749

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…According to Abualhaj et al [6], the possibility of using neural networks and fuzzy logic methods for organizing queue management is substantiated. A compromise option from the point of view of the level of adequacy of the mathematical description of queue management processes and the expected computational complexity of the resulting solutions can be the approach presented in [7], [8]. It is based on the optimization problem statement of queue management in terms of balanced use of the available resource and queue utilization.…”

Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

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Model of optimal distribution of network resources with constraints on quality of service indicators

Ulugbek

Azamat

2023

Bulletin EEI

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Existing algorithms and mathematical models of queuing at the nodes of a telecommunications network are considered in this paper. The necessity of coordinated solutions to problems of distribution of channel and buffer resources of the network is shown. A model for the optimal distribution of channel and buffer resources on network nodes has been developed. The optimization (minimization) criterion is the total average packet delay with constraints on the quality of service (QoS) indicators of heterogeneous flows. The optimization problem is presented as a constrained nonlinear programming problem and solved using the “fmincon” program of the optimization toolbox MATLAB package.

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Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

“…Where 𝜌 𝑖 is the utilization of the i-th queue 𝑖 = 1, 𝑁 ̅̅̅̅̅ : 𝜌 𝑖 = 𝑑 𝑖 𝑥 𝑖 𝐶 ; 𝑙 𝑖 is the capacity of buffer memory of the i-th queue: 𝑙 𝑖 = 𝑦 𝑖 𝐿. The average queue length is determined by (23) [6], [7]:…”

Section: The Proposed Model For the Distribution Of Channel And Buffe...mentioning

confidence: 99%

Model of optimal distribution of network resources with constraints on quality of service indicators

Ulugbek

Azamat

2023

Bulletin EEI

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“…The problem of modeling, managing and optimizing resources in a heterogeneous communication network is a very challenging engineering problem because of its inherent complexity ( [1], [2], [31]- [33]). Indeed, one of the most difficult challenges to be addressed to apply optimization techniques to the management of such complex networks is to derive predictive models of the queues of the switch behaviour ( [34]- [39]).…”

Section: Related Workmentioning

confidence: 99%

Modeling and Control of Priority Queueing in Software Defined Networks via Machine Learning

et al. 2022

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Software Defined Networking (SDN) is a new architectural paradigm that enables programmable control of a network to make it more flexible and easier to manage. SDN architectures decouple control and forwarding functionalities, and enable switches and routers to be remotely configurable/programmable in run-time by a controller. Modeling and optimization of such modern heterogeneous network infrastructures are key factors to achieve better performance, e.g. in terms of traffic flow improvement while reducing bandwidth allocation. Identifying an accurate model of a network device in SDNs (e.g., a switch or a router) is crucial in order to apply advanced techniques such as Model Predictive Control (MPC). However, such a problem is very challenging due to non-linearities and unavailability of internal variables measurements in real devices. To this aim, a promising direction is given by an appropriate integration of System Identification and Machine Learning techniques to obtain predictive models using historical data collected from the network thanks to the SDN paradigm. In this paper we apply a novel data-driven methodology to learn accurate models of the dynamical input-output behavior of a network's switch device by appropriately combining AutoRegressive eXogenous (ARX) model identification with Regression Trees (RTs) and Random Forests (RFs). The advantage of such model is that it can be directly used to apply MPC (which just requires Quadratic Programming to be solved) to optimally control the queues' bandwidth of the switch ports within the SDN paradigm. We validate our approach on an experimental emulation setup using the Mininet network emulator environment and a real dataset obtained from measurements of an Italian Internet Service Provider (Sonicatel S.r.l.). To this aim, we first develop a model of a real network switch, then implement MPC using the RYU controller, and finally demonstrate the benefits of the proposed dynamic queueing control methodology in terms of packet losses reduction and bandwidth saving, i.e. in terms of improvement of the Quality of Service.

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“…One of the main areas of theory and technology for dealing with passive and active disturbances is their compensation. In [3], a method for the distribution of telecommunication traffic with optimal usage of the available bandwidth was proposed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Signal Level Distribution in 5G Radio Relay Transmissions

et al. 2023

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Improvement of Method of Balanced Queue Management on Routers Interfaces of Telecommunication Networks

Cited by 7 publications

References 16 publications

Model of optimal distribution of network resources with constraints on quality of service indicators

Model of optimal distribution of network resources with constraints on quality of service indicators

Modeling and Control of Priority Queueing in Software Defined Networks via Machine Learning

Evaluation of Signal Level Distribution in 5G Radio Relay Transmissions

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