(Ebook) Dynamic Resource Management in Service-Oriented Core Networks by Zhuang, Weihua, Qu, Kaige ISBN 9783030871352, 3030871355

(Ebook) Dynamic Resource Management in Service-Oriented Core Networks by Zhuang, Weihua, Qu, Kaige ISBN 9783030871352, 3030871355

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. The service-oriented 5G and beyond core networks are featured by customized network services with differentiated quality-of-service (QoS) requirements, which can be provisioned through network slicing enabled by the software defined networking (SDN) and network function virtualization (NFV) paradigms. Multiple network services are embedded in a common physical infrastructure, generating service-customized network slices. Each network slice supports a composite service via virtual network function (VNF) chaining, with dedicated packet processing functionality at each VNF. For a network slice with a target traffic load, the E2E service delivery is enabled by VNF placement at NFV nodes (e.g., data centers and commodity servers) and traffic routing among corresponding NFV nodes, 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. 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. 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. 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 decisions, 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, we 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.