This book provides a timely and comprehensive study of dynamic resource management for network slicing in service-oriented fifth-generation (5G) and beyond core networks. This includes the perspective of developing efficient computation resource provisioning and scheduling solutions to guarantee consistent service performance in terms of end-to-end (E2E) data delivery delay.
Network slicing is enabled by the software defined networking (SDN) and network function virtualization (NFV) paradigms. For a network slice with a target traffic load, the E2E service delivery is enabled by virtual network function (VNF) placement and traffic routing with static resource allocations. When data traffic enters the network, the traffic load is dynamic and can deviate from the target value, potentially leading to QoS performance degradation and network congestion. Data traffic has dynamics in different time granularities. For example, the traffic statistics (e.g., mean and variance) can be non-stationary and experience significant changes in a coarse time granularity, which are usually predictable. Within a long time duration with stationary traffic statistics, there are traffic dynamics in small timescales, which are usually highly bursty and unpredictable. To provide continuous QoS performance guarantee and ensure efficient and fair operation of the network slices over time, it is essential to develop dynamic resource management schemes for the embedded services in the presence of traffic dynamics during virtual network operation. Queueing theory is used in system modeling, and different techniques including optimization and machine learning are applied to solving the dynamic resource management problems.
Based on a simplified M/M/1 queueing model with Poisson traffic arrivals, an optimization model for flow migration is presented to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. To overcome the limitations of Poisson traffic model, the authors present a machine learning approach for dynamic VNF resource scaling and migration. The new solution captures the inherent traffic patterns in a real-world traffic trace with non-stationary traffic statistics in large timescale, predicts resource demands for VNF resource scaling, and triggers adaptive VNF migration decision making, to achieve load balancing, migration cost reduction, and resource overloading penalty suppression in the long run. Both supervised and unsupervised machine learning tools are investigated for dynamic resource management. To accommodate the traffic dynamics in small time granularities, the authors present a dynamic VNF scheduling scheme to coordinate the scheduling among VNFs of multiple services, which achieves network utility maximization with delay guarantee for each service. Researchers and graduate students working in the areas of electrical engineering, computing engineering and computer science will find this book useful as a reference or secondary text. Professionals in industry seeking solutions to dynamic resource management for 5G and beyond networks will also want to purchase this book.

Presents research on dynamic resource management for network slicing in service-oriented 5G and beyond core networks Introduces applications of machine learning techniques in dynamic resource management Includes advanced algorithms and solutions for dynamic resource management

Autorentext

Qiang Ye received the Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, ON, Canada, in 2016. From December 2016 to September 2019, he was a Postdoctoral Fellow and a Research Associate with the Department of Electrical and Computer Engineering, University of Waterloo. Since September 2019, he has been an Assistant Professor with the Department of Electrical and Computer Engineering and Technology, Minnesota State University, Mankato, MN, USA. His research interests include network slicing for 5G networks, edge intelligence for autonomous vehicular networks, artificial intelligence for future networking, protocol design, and performance analysis for the Internet of Things. He was a Technical Program Committee (TPC) Members for several international conferences, including the IEEE GLOBECOM'20, VTC'17, VTC'20, and ICPADS'20. He is an editor of the International Journal of Distributed Sensor Networks (SAGE Publishing) and Wireless Networks (SpringerNature), and an Area Editor of the Encyclopedia of Wireless Networks (SpringerNature). Weihua Zhuang received the Ph.D. degree in electrical engineering in 1993 from the University of New Brunswick, Canada. She has been with the Department of Electrical and Computer Engineering, University of Waterloo, Canada, since 1993, where she is a University Professor and a Tier I Canada Research Chair in Wireless Communication Networks. Dr. Zhuang was a recipient of the 2021 Women's Distinguished Career Award from the IEEE Vehicular Technology Society, 2021 R. A. Fessenden Award from the IEEE Canada, 2017 Technical Recognition Award in Ad Hoc and Sensor Networks from the IEEE Communications Society, and a co-recipient of several Best Paper Awards from international conferences. She was the Editor-in-Chief of the IEEE Transactions on Vehicular Technology from 2007 to 2013, Technical Program Chair/Co-Chair of IEEE VTC 2017/2016 Fall, and Technical Program Symposia Chair of IEEEGlobecom 2011. She is an elected member of the Board of Governors and Vice President for Publications of the IEEE Vehicular Technology Society. She was an IEEE Communications Society Distinguished Lecturer from 2008 to 2011. Dr. Zhuang is a Fellow of the IEEE, Royal Society of Canada, Canadian Academy of Engineering, and Engineering Institute of Canada.

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Chapter 1 Introduction-. 1.1 Service-Oriented Core Networks-. 1.1.1 Software-Defined Networking (SDN)-. 1.1.2 Network Function Virtualization (NFV)-. 1.1.3 Service Function Chaining-. 1.2 Network Slicing Framework-. 1.2.1 Infrastructure Domain-. 1.2.2 Tenant Domain-. 1.2.3 SDN-NFV Integration-. 1.3 Multi-Timescale Dynamic Resource Management-. 1.3.1 Multi-Timescale Core Network Traffic Dynamics-. 1.3.2 Dynamic Resource Provisioning in Large Timescale-. 1.3.3 Dynamic Resource Scheduling in Small Timescale-. 1.4 Research Contributions-. 1.5 Outline-. References-. Chapter 2 System Model-. 2.1 Services-. 2.2 Virtual Resource Pool-. 2.3 Placement and Scheduling of Virtual Network Function (VNF)-. 2.3 Migration Cost and Reconfiguration Overhead-. References-. Chapter 3 Dynamic Flow Migration: A Model-Based Optimization Approach-. 3.1 Model Assumptions-. 3.1.1 M/M/1 VNF Packet Processing Queueing Model-. 3.1.2 Generalized Processor Sharing (GPS)-. 3.2 Optimization Model for Dynamic Flow Migration-. 3.3 Mixed Integer Quadratically Constrained Programming (MIQCP) Problem Transformation-. 3.3.1 Optimality Gap-. 3.3.2 Optimal Solution Mapping-. 3.4 Low-Complexity Heuristic Flow Migration Algorithm-. 3.4.1 Algorithm Overview-. 3.4.2 Redistribution of Hop Delay Bounds-. 3.4.3 Migration Decision-. 3.4.4 Iterative Resource Loading Threshold Update-. 3.4.5 Complexity Analysis-. 3.5 Simulation Results-. 3.6 Summary-. References-. Chapter 4 Dynamic VNF Resource Scaling and Migration: A Machine Learning Approach-. 4.1 Nonstationary Traffic Model-. 4.2 Machine Learning Tools for Analysis and Decision-. 4.2.1 Bayesian Conjugate Analysis-. 4.2.2 Gaussian Process Regression-. 4.2.3 Reinforcement Learning-. 4.3 Resource Demand Prediction for Dynamic VNF Resource Scaling-. 4.3.1 Bayesian Online Change Point Detection-. 4.3.2 Traffic Parameter Learning-. 4.3.3 Resource Demand Prediction-. 4.4 Deep Reinforcement Learning for Dynamic VNF Migration-. 4.4.1 MarkovDecision Process-. 4.4.2 Penalty-Aware Deep Q-Learning Algorithm-. 4.5 Simulation Results-. 4.6 Summa…