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.
