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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Dynamic Resource Management in Service-Oriented Core Networks (Hardcover, 1st ed. 2021)
Loot Price: R3,794
Discovery Miles 37 940
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Dynamic Resource Management in Service-Oriented Core Networks (Hardcover, 1st ed. 2021)
Series: Wireless Networks
Expected to ship within 12 - 17 working days
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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.
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