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QoS-aware virtual network embedding

Jiang, Chunxiao.

QoS-aware virtual network embedding

Record Type:	Electronic resources : Monograph/item
Title/Author:	QoS-aware virtual network embeddingby Chunxiao Jiang, Peiying Zhang.
Author:	Jiang, Chunxiao.
other author:	Zhang, Peiying.
Published:	Singapore :Springer Singapore :2021.
Description:	x, 401 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Quality of service (Computer networks)
Online resource:	https://doi.org/10.1007/978-981-16-5221-9
ISBN:	9789811652219$q(electronic bk.)

QoS-aware virtual network embedding
Jiang, Chunxiao.

QoS-aware virtual network embedding[electronic resource] /by Chunxiao Jiang, Peiying Zhang. - Singapore :Springer Singapore :2021. - x, 401 p. :ill., digital ;24 cm.

Chapter 1. Introduction -- Chapter 2. Introduction of security requirements in VNE -- Chapter 3. Security Aware Virtual Network Embedding Algorithm using Information Entropy TOPSIS -- Chapter 4. Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning -- Chapter 5. VNE Solution for Network Differentiated QoS and Security Requirements From the Perspective of Deep Reinforcement Learning -- Chapter 6. Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm -- Chapter 7. Description of service-aware requirements in VNE -- Chapter 8. Virtual Network Embedding based on Modified Genetic Algorithm -- Chapter 9. VNE-HPSO Virtual Network Embedding Algorithm based On Hybrid Particle Swarm Optimization -- Chapter 10. Topology based Reliable Virtual Network Embedding from a QoE Perspective -- Chapter 11. "DSCD Delay Sensitive Cross-Domain Virtual Network Embedding Algorithm" -- Chapter 12."A Multi-Domain Virtual Network Embedding Algorithm with Delay Prediction" -- Chapter 13. "Description of energy consumption requirements in VNE" -- Chapter 14. A"Multi-objective Enhanced Particle Swarm Optimization in Virtual Network Embedding" -- Chapter 15. "Incorporating Energy and Load Balance into Virtual Network Embedding Process" -- Chapter 16. "IoV Scenario Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode" -- Chapter 17. Description of load balance in VNE -- Chapter 18."A Multi-Domain VNE Algorithm based on Load Balancing in the IoT Networks" -- Chapter 19."Virtual Network Embedding based on Computing, Network and Storage Resource Constraints" -- Chapter 20. Virtual Network Embedding using Node Multiple Metrics based on Simplified ELECTRE Method -- Chapter 21."VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making" -- Chapter 22. Conclusion.

As an important future network architecture, virtual network architecture has received extensive attention. Virtual network embedding (VNE) is one of the core services of network virtualization (NV) It provides solutions for various network applications from the perspective of virtual network resource allocation. The Internet aims to provide global users with comprehensive coverage. The network function requests of hundreds of millions of end users have brought great pressure to the underlying network architecture. VNE algorithm can provide effective support for the reasonable and efficient allocation of network resources, so as to alleviate the pressure off the Internet. At present, a distinctive feature of the Internet environment is that the quality of service (QoS) requirements of users are differentiated. Different regions, different times, and different users have different network function requirements. Therefore, network resources need to be reasonably allocated according to users' QoS requirements to avoid the waste of network resources. In this book, based on the analysis of the principle of VNE algorithm, we provide a VNE scheme for users with differentiated QoS requirements. We summarize the common user requirements into four categories: security awareness, service awareness, energy awareness, and load balance, and then introduce the specific implementation methods of various differentiated QoS algorithms. This book provides a variety of VNE solutions, including VNE algorithms for single physical domain, VNE algorithms for across multiple physical domains, VNE algorithms based on heuristic method, and VNE algorithms based on machine learning method.

ISBN: 9789811652219$q(electronic bk.)

Standard No.: 10.1007/978-981-16-5221-9doiSubjects--Topical Terms:

680487
Quality of service (Computer networks)

LC Class. No.: TK5105.548 / .J53 2021

Dewey Class. No.: 004.6

QoS-aware virtual network embedding
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505 0 $a Chapter 1. Introduction -- Chapter 2. Introduction of security requirements in VNE -- Chapter 3. Security Aware Virtual Network Embedding Algorithm using Information Entropy TOPSIS -- Chapter 4. Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning -- Chapter 5. VNE Solution for Network Differentiated QoS and Security Requirements From the Perspective of Deep Reinforcement Learning -- Chapter 6. Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm -- Chapter 7. Description of service-aware requirements in VNE -- Chapter 8. Virtual Network Embedding based on Modified Genetic Algorithm -- Chapter 9. VNE-HPSO Virtual Network Embedding Algorithm based On Hybrid Particle Swarm Optimization -- Chapter 10. Topology based Reliable Virtual Network Embedding from a QoE Perspective -- Chapter 11. "DSCD Delay Sensitive Cross-Domain Virtual Network Embedding Algorithm" -- Chapter 12."A Multi-Domain Virtual Network Embedding Algorithm with Delay Prediction" -- Chapter 13. "Description of energy consumption requirements in VNE" -- Chapter 14. A"Multi-objective Enhanced Particle Swarm Optimization in Virtual Network Embedding" -- Chapter 15. "Incorporating Energy and Load Balance into Virtual Network Embedding Process" -- Chapter 16. "IoV Scenario Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode" -- Chapter 17. Description of load balance in VNE -- Chapter 18."A Multi-Domain VNE Algorithm based on Load Balancing in the IoT Networks" -- Chapter 19."Virtual Network Embedding based on Computing, Network and Storage Resource Constraints" -- Chapter 20. Virtual Network Embedding using Node Multiple Metrics based on Simplified ELECTRE Method -- Chapter 21."VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making" -- Chapter 22. Conclusion.
520 $a As an important future network architecture, virtual network architecture has received extensive attention. Virtual network embedding (VNE) is one of the core services of network virtualization (NV) It provides solutions for various network applications from the perspective of virtual network resource allocation. The Internet aims to provide global users with comprehensive coverage. The network function requests of hundreds of millions of end users have brought great pressure to the underlying network architecture. VNE algorithm can provide effective support for the reasonable and efficient allocation of network resources, so as to alleviate the pressure off the Internet. At present, a distinctive feature of the Internet environment is that the quality of service (QoS) requirements of users are differentiated. Different regions, different times, and different users have different network function requirements. Therefore, network resources need to be reasonably allocated according to users' QoS requirements to avoid the waste of network resources. In this book, based on the analysis of the principle of VNE algorithm, we provide a VNE scheme for users with differentiated QoS requirements. We summarize the common user requirements into four categories: security awareness, service awareness, energy awareness, and load balance, and then introduce the specific implementation methods of various differentiated QoS algorithms. This book provides a variety of VNE solutions, including VNE algorithms for single physical domain, VNE algorithms for across multiple physical domains, VNE algorithms based on heuristic method, and VNE algorithms based on machine learning method.
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