國立虎尾科技大學 |

Dynamic Resource Management in Service-Oriented Core Networks

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Dynamic Resource Management in Service-Oriented Core Networks/ by Weihua Zhuang, Kaige Qu.
作者:	Zhuang, Weihua.
其他作者:	Qu, Kaige.
面頁冊數:	XII, 173 p. 189 illus., 59 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-87136-9
ISBN:	9783030871369

Dynamic Resource Management in Service-Oriented Core Networks
Zhuang, Weihua.

Dynamic Resource Management in Service-Oriented Core Networks[electronic resource] /by Weihua Zhuang, Kaige Qu. - 1st ed. 2021. - XII, 173 p. 189 illus., 59 illus. in color.online resource. - Wireless Networks,2366-1445. - Wireless Networks,.

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.

ISBN: 9783030871369

Standard No.: 10.1007/978-3-030-87136-9doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: TK5105.5-5105.9

Dewey Class. No.: 004.6

Dynamic Resource Management in Service-Oriented Core Networks
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