LEADER 03836nam 22006135 450 001 9910508474503321 005 20250820220205.0 010 $a3-030-87136-3 024 7 $a10.1007/978-3-030-87136-9 035 $a(CKB)4940000000615873 035 $a(MiAaPQ)EBC6798607 035 $a(Au-PeEL)EBL6798607 035 $a(OCoLC)1285071337 035 $a(PPN)258841567 035 $a(DE-He213)978-3-030-87136-9 035 $a(EXLCZ)994940000000615873 100 $a20211103d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDynamic Resource Management in Service-Oriented Core Networks /$fby Weihua Zhuang, Kaige Qu 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (182 pages) 225 1 $aWireless Networks,$x2366-1445 311 08$a3-030-87135-5 330 $aThis book provides a timely and comprehensive study of dynamic resource management for network slicing in service-oriented fifth-generation (5G) and beyond core networks. This includes the perspective of developing efficient computation resource provisioning and scheduling solutions to guarantee consistent service performance in terms of end-to-end (E2E) data delivery delay. Based on a simplified M/M/1 queueing model with Poisson traffic arrivals, an optimization model for flow migration is presented to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. To overcome the limitations of Poisson traffic model, the authors present a machine learning approach for dynamic VNF resource scaling and migration. The new solution captures the inherent traffic patterns in a real-world traffic trace with non-stationary traffic statistics in large timescale, predicts resource demands for VNF resource scaling, and triggers adaptive VNF migration decision making, to achieve load balancing, migration cost reduction, and resource overloading penalty suppression in the long run. Both supervised and unsupervised machine learning tools are investigated for dynamic resource management. To accommodate the traffic dynamics in small time granularities, the authors present a dynamic VNF scheduling scheme to coordinate the scheduling among VNFs of multiple services, which achieves network utility maximization with delay guarantee for each service. Researchers and graduate students working in the areas of electrical engineering, computing engineering and computer science will find this book useful as a reference or secondary text. Professionals in industry seeking solutions to dynamic resource management for 5G and beyond networks will also want to purchase this book. 410 0$aWireless Networks,$x2366-1445 606 $aComputer networks 606 $aWireless communication systems 606 $aMobile communication systems 606 $aMachine learning 606 $aComputer Communication Networks 606 $aWireless and Mobile Communication 606 $aMachine Learning 615 0$aComputer networks. 615 0$aWireless communication systems. 615 0$aMobile communication systems. 615 0$aMachine learning. 615 14$aComputer Communication Networks. 615 24$aWireless and Mobile Communication. 615 24$aMachine Learning. 676 $a384.54524015193 700 $aZhuang$b Weihua$01052122 702 $aQu$b Kaige 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910508474503321 996 $aDynamic Resource Management in Service-Oriented Core Networks$92568058 997 $aUNINA