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  • © 2021

Dynamic Resource Management in Service-Oriented Core Networks

  • 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

Part of the book series: Wireless Networks (WN)

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  • ISBN: 978-3-030-87136-9
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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xii
  2. Introduction

    • Weihua Zhuang, Kaige Qu
    Pages 1-20
  3. Literature Review

    • Weihua Zhuang, Kaige Qu
    Pages 21-30
  4. Dynamic VNF Scheduling for Network Utility Maximization

    • Weihua Zhuang, Kaige Qu
    Pages 131-156
  5. Conclusions and Future Research Directions

    • Weihua Zhuang, Kaige Qu
    Pages 157-159
  6. Back Matter

    Pages 161-173

About this book

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.

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.

Keywords

  • 5G Networks
  • Dynamic Resource Management
  • Network Slicing
  • Software-Defined Networks (SDN)
  • Network Function Virtualization (NFV)
  • Service Function Chain (SFC)
  • Quality-of-Service (QoS) Provisioning
  • Delay-Sensitive Services
  • Queueing Model
  • Optimization, Stochastic/Lyapunov Optimization
  • Machine Learning
  • software defined networking (SDN)
  • end-to-end (E2E) delay
  • virtual network function (VNF)
  • traffic dynamics
  • non-stationary traffic
  • dynamic flow migration

Authors and Affiliations

  • Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada

    Weihua Zhuang, Kaige Qu

About the authors

Dr. Weihua Zhuang 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. She was a recipient of the 2021 Women's Distinguished Career Award from the Vehicular Technology Society of the Institute of Electrical and Electronics Engineers (IEEE), 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. She was the Editor-in-Chief of the IEEE Transactions on Vehicular Technology from 2007 to 2013, Technical Program Chair/Co-Chair of the 2017 and 2016 IEEE Vehicular Technology Conferences, and Technical Program Symposia Chair of the 2011 IEEE Global Communications Conference. She is an elected member of the Board of Governors and Vice President for Publications of the IEEE Vehicular Technology Society. Dr. Zhuang is a Fellow of the IEEE, Royal Society of Canada, Canadian Academy of Engineering, and Engineering Institute of Canada.

Dr. Kaige Qu received the B.Sc. degree in communication engineering from Shandong University, Jinan, China, in 2013, the M.Sc. degree in integrated circuits engineering and electrical engineering from Tsinghua University, Beijing, China, and KU Leuven, Leuven, Belgium, respectively, in 2016, and the Ph.D. degree in electrical and computer engineering from the University of Waterloo, Waterloo, Ontario, Canada, in 2020. She is currently a Postdoctoral Fellow with the University of Waterloo. Her research interests include resource allocation in SDN/NFV-enabled networks, mobile edge computing, and artificial intelligence in networking.

Bibliographic Information

  • Book Title: Dynamic Resource Management in Service-Oriented Core Networks

  • Authors: Weihua Zhuang, Kaige Qu

  • Series Title: Wireless Networks

  • DOI: https://doi.org/10.1007/978-3-030-87136-9

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

  • Hardcover ISBN: 978-3-030-87135-2

  • eBook ISBN: 978-3-030-87136-9

  • Series ISSN: 2366-1186

  • Series E-ISSN: 2366-1445

  • Edition Number: 1

  • Number of Pages: XII, 173

  • Number of Illustrations: 130 b/w illustrations, 59 illustrations in colour

  • Topics: Computer Communication Networks, Wireless and Mobile Communication, Machine Learning

Buying options

eBook USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-87136-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 159.99
Price excludes VAT (USA)