Problems, Models and Algorithms
Buch, Englisch, 135 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 236 g
ISBN: 978-981-19-4817-6
Verlag: Springer Nature Singapore
Network Function Virtualization (NFV) has recently attracted considerable attention from both research and industrial communities. Numerous papers have been published regarding solving the resource- allocation problems in NFV, from various perspectives, considering different constraints, and adopting a range of techniques. However, it is difficult to get a clear impression of how to understand and classify different kinds of resource allocation problems in NFV and how to design solutions to solve these problems efficiently.
This book addresses these concerns by offering a comprehensive overview and explanation of different resource allocation problems in NFV and presenting efficient solutions to solve them. It covers resource allocation problems in NFV, including an introduction to NFV and QoS parameters modelling as well as related problem definition, formulation and the respective state-of-the-art algorithms.This book allows readers to gain a comprehensive understanding of and deep insights into the resource allocation problems in NFV. It does so by exploring (1) the working principle and architecture of NFV, (2) how to model the Quality of Service (QoS) parameters in NFV services, (3) definition, formulation and analysis of different kinds of resource allocation problems in various NFV scenarios, (4) solutions for solving the resource allocation problem in NFV, and (5) possible future work in the respective area.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Chapter 1 An Introduction to NFV.- Chapter 2 Resource Allocation Problems Formulation and Analysis in NFV.- Chapter 3 Delay-Aware and Availability-Aware VNF placement and routing.- Chapter 4 VNF placement and routing in edge clouds.- Chapter 5 Traffic Routing in Stochastic NFV Networks.- Chapter 6 Online Virtual Network Function Control Across Geo-Distributed Datacenters.- Chapter 7 Deep Reinforcement Learning for NFV.- Chapter 8 Future Work and Summarization.