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portada Qos-Aware Virtual Network Embedding
Type
Physical Book
Author
Publisher
Language
English
Pages
401
Format
Paperback
Dimensions
23.4x15.6x2.1 cm
Weight
0.58 kg.
ISBN13
9789811652233
Categories

Qos-Aware Virtual Network Embedding

Chunxiao Jiang (Author) · Peiying Zhang (Author) · Springer · Paperback

Qos-Aware Virtual Network Embedding - Jiang, Chunxiao ; Zhang, Peiying

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Synopsis "Qos-Aware Virtual Network Embedding "

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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