Téléchargez le livre : Bringing Machine Learning to Software-Defined Networks

Bringing Machine Learning to Software-Defined Networks

de

Éditeur :

Springer


Collection :

SpringerBriefs in Computer Science

Paru le : 2022-10-05

eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
52,74 €
Téléchargement immédiat
Dès validation de votre commande
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

badge apple Application Louise Reader
badge Android Application Louise Reader
Description
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.
Pages
68 pages
Collection
SpringerBriefs in Computer Science
Parution
2022-10-05
Marque
Springer
EAN papier
9789811948732
EAN PDF
9789811948749
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
6
Taille du fichier
3860 Ko
Prix
52,74 €
EAN EPUB
9789811948749
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
6
Taille du fichier
9400 Ko
Prix
52,74 €

Suggestions personnalisées