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Traffic engineering method in large-scale software-defined networks

Traffic engineering method in large-scale software-defined networks

Abstract

This paper is devoted to the method of traffic engineering in software-defined networks of large scalability. A review and comparison of existing solutions is carried out, on the basis of which a modified method of traffic engineering is proposed, which takes into account the software-defined architecture with regard to the load parameter. The modified method takes into account the cluster structure of the network and provides a two-layer organization of route calculation. Each local cluster controller exchanges information with the central controller responsible for calculating and managing network traffic. This interaction of components allows to effectively manage the transmission of traffic along the least loaded route. The proposed method uses available communication channels and reduces the waiting time for data transmission in the context of network scaling by selecting less congested links. Робота присвячена способу конструювання трафіку в програмно-конфігурованих мережах великої розмірності. Здійснено огляд та порівняння існуючих рішень на основі яких запропоновано модифікований спосіб конструювання трафіку, що враховує програмно-конфігуровану архітектуру з урахуванням метрики загрузки. Модифікований спосіб враховувує кластерну структуру мережі та забезпечує двошарову організацію обчислення маршрутів. Кожен локальний контролер кластеру обмінюється інформацією з центральним контролером, що відповідає за обрахунок та керування трафіком мережі. Така взаємодія компонентів дозволяє ефективно керувати передачею трафіку по найменш навантаженому маршруту. Запропонований спосіб використовує доступні канали зв'язку та зменшує час очікування передачі даних в умовах масштабування мережі шляхом вибору менш загружених ділянок.

Keywords

програмно-конфігуровані мережі, багатошляхова маршрутизація, traffic engineering, multipath routing, конструювання трафіку, software-defined networks, K-means algorithm, алгоритм К-середніх

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
bronze
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