Intelligent Resource Management for Network Slicing in 5G and Beyond (Wireless Networks)

✍ Scribed by Qiang Ye, Weihua Zhuang

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 202
• Category: Library

✦ Synopsis

This book provides a timely and comprehensive study of developing efficient network slicing frameworks in both 5G wireless and core networks. It also presents protocol stack layer perspectives, which includes virtual network topology design, end-to-end delay modeling, dynamic resource slicing, and link-layer and transport-layer protocol customization.

This book provides basic principles, concepts and technologies for communication, computing and networking. Optimization and queueing analysis techniques are applied to solving different problems for network slicing illustrated in this book as well.

Researchers working in the area of network slicing in 5G networks and beyond, and advanced-level students majoring in electrical engineering, computer engineering and computer science will find this book useful as a reference or secondary textbook. Professionals in industry seeking solutions to resource management for 5G networks and beyond will also want to purchase thisbook.

✦ Table of Contents

Preface
Contents
Acronyms
1 Introduction
1.1 5G Networks
1.1.1 Internet-of-Things Era
1.1.2 5G Wireless Networks
1.1.3 5G Core Networks
1.2 Resource Management in 5G Networks
1.2.1 Network Slicing
1.2.2 Virtual Network Topology
1.2.3 Resource Slicing
1.2.4 Customized Protocol
1.3 Summary
References
2 Virtual Network Topology Design and E2E Delay Modeling
2.1 Multi-Service Virtual Network Topology Design
2.1.1 Physical Substrate Network
2.1.2 Multicast SFCs
2.1.3 Problem Formulation
2.1.3.1 Multipath-Enabled Single Service Scenario
2.1.3.2 Multipath-Enabled Multi-Service Scenario
2.1.4 Heuristic Solution
2.1.4.1 Single-Service Scenario
2.1.4.2 Multi-Service Scenario
2.1.5 Performance Evaluation
2.2 E2E Delay Modeling for Embedded SFCs
2.2.1 Service Function Chaining
2.2.2 Resource Consumption Profile
2.2.3 Dominant-Resource Generalized Processor Sharing
2.2.4 E2E Delay Modeling
2.2.4.1 Processing and Transmission Rate Decoupling
2.2.4.2 Delay Modeling at the First NFV Node
2.2.4.3 Delay over the Virtual Link Between NFV Nodes
2.2.4.4 Average E2E Delay
2.2.5 Performance Evaluation
2.2.5.1 Delay at the First NFV Node
2.2.5.2 Delay at Subsequent NFV Nodes
2.3 Summary
References
3 Dynamic Resource Slicing for Service Provisioning
3.1 Bi-resource Slicing for 5G Core Networks
3.1.1 Performance Comparison
3.2 Dynamic Radio Resource Slicing for 5G Wireless Networks
3.2.1 System Model
3.2.2 Dynamic Radio Resource Slicing Framework
3.2.3 Problem Formulation
3.2.3.1 Optimizing Resource Allocation for MUs and MTDs
3.2.4 Problem Transformation for Partial Optimal Solutions
3.2.5 Simulation Results
3.2.5.1 Optimal Radio Resource Slicing Ratios
3.2.5.2 Performance Comparison
3.3 Summary
References
4 Transport-Layer Protocol Customization for 5G Core Networks
4.1 SDN/NFV-Based Transmission Protocol Customization
4.2 Protocol Customization for Time-Critical Services
4.2.1 Network Model
4.2.2 Network Functionalities
4.2.2.1 Caching Function
4.2.2.2 Retransmission Function
4.2.3 Traffic Model
4.2.4 Customized Protocol Design
4.2.4.1 Connection Establishment
4.2.4.2 Data Transmission
4.2.4.3 Packet Retransmission
4.2.4.4 Slice-Level Congestion Control
4.2.5 Optimized Probabilistic Caching Policy
4.2.5.1 Problem Formulation
4.2.5.2 Optimized Probabilistic Caching
4.2.5.3 Time Complexity
4.2.6 Numerical Results
4.2.6.1 Adaptive Probabilistic Caching Policy
4.2.6.2 E2E Packet Delay
4.3 Protocol Customization for Video-Streaming Services
4.3.1 Network Model
4.3.2 VoD Streaming System
4.3.3 Protocol Functionalities
4.3.4 Performance Metrics
4.3.5 Learning-Based Transmission Protocol
4.3.5.1 Protocol Framework
4.3.6 Video Traffic Prediction
4.3.6.1 Model Parameters
4.3.6.2 Traffic Prediction via ARIMA Model
4.3.7 MAB Learning Based Action Selection
4.3.8 Performance Evaluation
4.3.8.1 Simulation Settings
4.3.8.2 Numerical Results
4.4 Summary
References
5 Adaptive Medium Access Control Protocols for IoT-Enabled Mobile Networks
5.1 Load-Adaptive MAC with Homogeneous Traffic
5.1.1 Network Model
5.1.2 Adaptive MAC Framework
5.1.3 Closed-Form Performance Models for IEEE 802.11 DCF and D-TDMA
5.1.3.1 Closed-Form Performance Models for IEEE 802.11 DCF
5.1.3.2 Closed-Form Performance Models for D-TDMA
5.1.4 Adaptive MAC Solution
5.1.5 Numerical Results
5.1.5.1 Traffic Saturation Case
5.1.5.2 Traffic Non-saturation Case
5.2 Distributed and Service-Oriented MAC with Heterogeneous Traffic
5.2.1 System Model
5.2.2 The DAH-MAC Scheme
5.2.2.1 Accessing Minislots
5.2.2.2 Adaptive TDMA Slot Allocation
5.2.2.3 T-CSMA/CA Based Contention Access
5.2.3 Performance Analysis
5.2.3.1 Voice Capacity
5.2.3.2 Average Number of Scheduled Voice Bursts in a CFP
5.2.3.3 A Data Throughput Optimization Framework for the DAH-MAC
5.2.4 Numerical Results
5.2.4.1 Voice Capacity
5.2.4.2 Number of Scheduled Voice Bursts (Time Slots) in a CFP
5.2.4.3 Voice Packet Loss Rate
5.2.4.4 Aggregate Best-Effort Data Throughput
5.3 Summary
References
6 Conclusion and Future Works
6.1 Learning-Based Resource Slicing for Beyond 5G Networks
6.2 Protocol Automation for Beyond 5G Networks
References
Appendices
Appendix A: Proof of Proposition 1
Appendix B: Proof of Proposition 2
Appendix C: Proof of Proposition 3
Appendix D: Proof of Proposition 4
Appendix E: Proof of Proposition 5
Appendix F: Packet Loss Detection Thresholds
Appendix G: Methodology for Packet Loss Detection
Appendix H: Proof of Proposition 6
Appendix I: Derivation of E[Wst] and E[Wqt]
References
Index