Bringing Machine Learning to Software-Defined Networks

Name: Bringing Machine Learning to Software-Defined Networks
Brand: Springer, Berlin
SKU: 9789811948732
Price: 276.84 RON
Availability: InStock

Autor: Zehua Guo

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Legare: Carte broșată

Editura: Springer, Berlin

Disponibilitate: În depozitul extern

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Emerging machine learning techniques bring new opportunities to flexible network control and managem...

Informații despre carte

Autor

Zehua Guo

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2022

Pagini

EAN

9789811948732

Enbook ID

39341639

Editura

Springer, Berlin

Greutate

131

Dimensiuni

155 x 235 x 6

Descriere completă

Emerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load balancing in software-defined wide area networks, as well as flow scheduling, coflow scheduling, and flow migration for network function virtualization in software-defined data center networks. It helps readers reflect on several practical problems of deploying SDN and learn how to solve the problems by taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.

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Bringing Machine Learning to Software-Defined Networks

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