Qos Aware Virtual Network Embedding

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QoS-Aware Virtual Network Embedding

QoS-Aware Virtual Network Embedding
Author :
Publisher : Springer Nature
Total Pages : 395
Release :
ISBN-10 : 9789811652219
ISBN-13 : 981165221X
Rating : 4/5 (21X Downloads)

Book Synopsis QoS-Aware Virtual Network Embedding by : Chunxiao Jiang

Download or read book QoS-Aware Virtual Network Embedding written by Chunxiao Jiang and published by Springer Nature. This book was released on 2022-01-01 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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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