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.
Author(s): Song Yang, Nan He, Fan Li, Xiaoming Fu
Publisher: Springer
Year: 2022
Language: English
Pages: 142
City: Singapore
Preface
Acknowledgments
Contents
About the Authors
1 Introduction
1.1 The Advantages and Disadvantages of NFV
1.2 NFV Standard and SDN
1.3 Book Organization
References
2 Resource Allocation Problems Formulation and Analysis
2.1 Basic Problem Definition and Analysis
2.1.1 Examples
2.1.2 Problem Goals
2.1.3 Problem Formulation
2.1.4 Mainly Adopted Approaches
2.2 QoS Models in NFV
2.2.1 Delay Calculation of an SFC
2.2.2 NFV Resilience
2.3 Summary
References
3 Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds
3.1 Introduction
3.2 Related Work
3.2.1 Traffic/Cost-Aware VNF Placement and Routing in Generic Networks
3.2.2 Delay-Aware VNF Placement and Routing in Generic Networks
3.2.3 Delay-Aware VNF/Rule Placement and Routing in Edge Clouds and Software Defined Networks (SDN)
3.3 Network Delay Model
3.3.1 Service Function Chaining
3.3.2 Traversing Delay in a SFC in Edge Clouds
3.4 Problem Definition and Complexity Analysis
3.4.1 Problem Definition
3.4.2 An Exact Formulation
3.4.3 Complexity Analysis
3.5 Approximation Algorithm
3.5.1 Transformation from the INLP to the LP
3.5.2 Randomized Rounding Approximation Algorithm
3.5.3 Approximation Performance Analysis
3.5.3.1 Link Capacity Violating Factor
3.6 Simulations
3.6.1 Simulation Setup
3.6.2 Simulation Results
3.7 Summary
References
4 Delay-Sensitive and Availability-Aware Virtual Network Function Scheduling for NFV
4.1 Introduction
4.2 Related Work
4.2.1 Traffic and Cost-Aware VNF Placement and Routing
4.2.2 Delay-Sensitive VNF Scheduling
4.2.3 NFV Resiliency
4.3 Delay Calculation for a flow in a Service Function Chaining
4.3.1 Totally Ordered SFC
4.3.2 Partially Ordered SFC
4.4 VNF Placement Availability Calculation
4.5 Problem Definition and Complexity Analysis
4.5.1 Delay-Sensitive VNF Scheduling
4.5.2 Delay-Sensitive and Availability-Aware VNF Scheduling
4.5.3 Complexity Analysis
4.6 Solutions
4.6.1 Exact Solution
4.6.2 Heuristic
4.7 Simulations
4.7.1 Simulation Setup
4.7.2 Simulation Results
4.7.2.1 Delay-Sensitive VNF Scheduling
4.7.2.2 Delay-Sensitive and Availability-Aware VNF Scheduling
4.8 Summary
References
5 Traffic Routing in Stochastic Network Function Virtualization Networks
5.1 Introduction
5.2 Related Work
5.2.1 Traffic Routing and VNF Placement in Deterministic NFV Networks
5.2.1.1 Traffic Routing in Deterministic NFV Networks
5.2.1.2 VNF Placement in Deterministic NFV Networks
5.2.1.3 VNF Placement and Routing in Deterministic NFV Networks
5.2.2 Traffic Routing and VNF Placement in Stochastic (NFV) Networks
5.3 Stochastic Link Weight
5.4 Problem Definition and Formulation
5.4.1 Problem Definition and Complexity Analysis
5.4.2 Problem Formulation
5.5 Multi-Constrained Traffic Routing Heuristic
5.6 Simulations
5.6.1 Simulation Setup
5.6.2 Simulation Results
5.6.2.1 Backbone Networks
5.6.2.2 100-Node Network
5.6.2.3 Varying the Maximum Number of Stored Paths for MCTR
5.7 Summary
References
6 A-DDPG: Attention Mechanism-Based Deep Reinforcement Learning for NFV
6.1 Introduction
6.2 Related Work
6.2.1 Combinatorial Optimization Theory for NFV
6.2.2 DRL for NFV
6.3 Model and Problem formulate
6.3.1 Network Utility Model
6.3.1.1 Operation Cost
6.3.1.2 Deployment Cost
6.3.1.3 Transmission Cost
6.3.2 Problem Definition and Formulation
6.4 Deep Reinforcement Learning
6.4.1 DRL Model Design
6.4.2 A-DDPG Framework
6.4.2.1 Attention Model
6.4.2.2 Actor-Critic Network Design
6.4.2.3 Algorithm Design
6.5 Performance Evaluation
6.5.1 Simulation Settings
6.5.2 Simulation Results
6.6 Summary
References
7 Summarization and Future Work
7.1 Summarization of This Book
7.2 Emerging Topics and Future Works
7.2.1 State-of-the-Art
7.2.2 Security-Aware Resource Allocation
7.2.3 Wireless Virtual Network Functions and Other Network Application Domains
7.2.4 Mobility Management in NFV
7.2.5 SDN and NFV
References
