A Reinforcement Learning Framework for Knowledge-Defined Networking

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253555" target="_blank" >RIV/61989100:27240/23:10253555 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/23:10253555

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10333287" target="_blank" >https://ieeexplore.ieee.org/document/10333287</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT61075.2023.10333287" target="_blank" >10.1109/ICUMT61075.2023.10333287</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Reinforcement Learning Framework for Knowledge-Defined Networking

Popis výsledku v původním jazyce

The deployment of 6G networks is expected to bring fundamental improvements to network architectures and a focus on incorporating artificial intelligence (AI) technologies. The paper explores the concept of knowledge-defined networking (KDN), where the intelligence of the network resides in the knowledge plane (KP), resulting from a combination of software-defined networking (SDN), network telemetry, and machine learning (ML) algorithms. The paper highlights the use of programming protocol-independent packet processors (P4), a technology which enables SDN networks, and emphasizes the importance of in-band network telemetry (INT) for providing real-time network information. The paper also draws a connection between P4-SDN network architecture and reinforcement Learning (RL), exhibiting how network components and existing techniques can be mapped onto RL principles. The potential of AI-driven network orchestration and the interpretation of networks as AI-based systems are also discussed.

Název v anglickém jazyce

A Reinforcement Learning Framework for Knowledge-Defined Networking

Popis výsledku anglicky

The deployment of 6G networks is expected to bring fundamental improvements to network architectures and a focus on incorporating artificial intelligence (AI) technologies. The paper explores the concept of knowledge-defined networking (KDN), where the intelligence of the network resides in the knowledge plane (KP), resulting from a combination of software-defined networking (SDN), network telemetry, and machine learning (ML) algorithms. The paper highlights the use of programming protocol-independent packet processors (P4), a technology which enables SDN networks, and emphasizes the importance of in-band network telemetry (INT) for providing real-time network information. The paper also draws a connection between P4-SDN network architecture and reinforcement Learning (RL), exhibiting how network components and existing techniques can be mapped onto RL principles. The potential of AI-driven network orchestration and the interpretation of networks as AI-based systems are also discussed.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

2157-0221

e-ISSN

—

Počet stran výsledku

5

Strana od-do

152-156

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Ghent

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—