Dynamic Resource Management in Service-Oriented Core Networks
portes grátis
Dynamic Resource Management in Service-Oriented Core Networks
Zhuang, Weihua; Qu, Kaige
Springer Nature Switzerland AG
11/2022
173
Mole
Inglês
9783030871383
15 a 20 dias
Descrição não disponível.
Chapter 1 Introduction-. 1.1 Service-Oriented Core Networks-. 1.1.1 Software-Defined Networking (SDN)-. 1.1.2 Network Function Virtualization (NFV)-. 1.1.3 Service Function Chaining-. 1.2 Network Slicing Framework-. 1.2.1 Infrastructure Domain-. 1.2.2 Tenant Domain-. 1.2.3 SDN-NFV Integration-. 1.3 Multi-Timescale Dynamic Resource Management-. 1.3.1 Multi-Timescale Core Network Traffic Dynamics-. 1.3.2 Dynamic Resource Provisioning in Large Timescale-. 1.3.3 Dynamic Resource Scheduling in Small Timescale-. 1.4 Research Contributions-. 1.5 Outline-. References-. Chapter 2 System Model-. 2.1 Services-. 2.2 Virtual Resource Pool-. 2.3 Placement and Scheduling of Virtual Network Function (VNF)-. 2.3 Migration Cost and Reconfiguration Overhead-. References-. Chapter 3 Dynamic Flow Migration: A Model-Based Optimization Approach-. 3.1 Model Assumptions-. 3.1.1 M/M/1 VNF Packet Processing Queueing Model-. 3.1.2 Generalized Processor Sharing (GPS)-. 3.2 Optimization Model for Dynamic Flow Migration-. 3.3 Mixed Integer Quadratically Constrained Programming (MIQCP) Problem Transformation-. 3.3.1 Optimality Gap-. 3.3.2 Optimal Solution Mapping-. 3.4 Low-Complexity Heuristic Flow Migration Algorithm-. 3.4.1 Algorithm Overview-. 3.4.2 Redistribution of Hop Delay Bounds-. 3.4.3 Migration Decision-. 3.4.4 Iterative Resource Loading Threshold Update-. 3.4.5 Complexity Analysis-. 3.5 Simulation Results-. 3.6 Summary-. References-. Chapter 4 Dynamic VNF Resource Scaling and Migration: A Machine Learning Approach-. 4.1 Nonstationary Traffic Model-. 4.2 Machine Learning Tools for Analysis and Decision-. 4.2.1 Bayesian Conjugate Analysis-. 4.2.2 Gaussian Process Regression-. 4.2.3 Reinforcement Learning-. 4.3 Resource Demand Prediction for Dynamic VNF Resource Scaling-. 4.3.1 Bayesian Online Change Point Detection-. 4.3.2 Traffic Parameter Learning-. 4.3.3 Resource Demand Prediction-. 4.4 Deep Reinforcement Learning for Dynamic VNF Migration-. 4.4.1 MarkovDecision Process-. 4.4.2 Penalty-Aware Deep Q-Learning Algorithm-. 4.5 Simulation Results-. 4.6 Summary-. References-. Chapter 5 Dynamic VNF Scheduling for Network Utility Maximization-. 5.1 Discrete-Time VNF Packet Processing Queueing Model-. 5.1.1 Physical Packet Processing Queue-. 5.1.2 Delay-Aware Virtual Packet Processing Queue-. 5.2 Stochastic VNF Scheduling: Problem and Solution-. 5.2.1 Stochastic Problem Formulation-. 5.2.2 Lyapunov Optimization and Problem Transformation-. 5.2.3 Online Distributed Algorithm-. 5.3 VNF Scheduling with Packet Rushing-. 5.3.1 Packet Rushing Analysis-. 5.3.2 Modified VNF Scheduling Algorithm-. 5.4 Simulation Results-. 5.5 Summary-. References-. Chapter 6 Conclusions and Future Research Directions-. 6.1 Conclusions-. 6.2 Future Research Directions-. References.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
5G Networks;Dynamic Resource Management;Network Slicing;Software-Defined Networks (SDN);Network Function Virtualization (NFV);Service Function Chain (SFC);Quality-of-Service (QoS) Provisioning;Delay-Sensitive Services;Queueing Model;Optimization, Stochastic/Lyapunov Optimization;Machine Learning;software defined networking (SDN);end-to-end (E2E) delay;virtual network function (VNF);traffic dynamics;non-stationary traffic;dynamic flow migration
Chapter 1 Introduction-. 1.1 Service-Oriented Core Networks-. 1.1.1 Software-Defined Networking (SDN)-. 1.1.2 Network Function Virtualization (NFV)-. 1.1.3 Service Function Chaining-. 1.2 Network Slicing Framework-. 1.2.1 Infrastructure Domain-. 1.2.2 Tenant Domain-. 1.2.3 SDN-NFV Integration-. 1.3 Multi-Timescale Dynamic Resource Management-. 1.3.1 Multi-Timescale Core Network Traffic Dynamics-. 1.3.2 Dynamic Resource Provisioning in Large Timescale-. 1.3.3 Dynamic Resource Scheduling in Small Timescale-. 1.4 Research Contributions-. 1.5 Outline-. References-. Chapter 2 System Model-. 2.1 Services-. 2.2 Virtual Resource Pool-. 2.3 Placement and Scheduling of Virtual Network Function (VNF)-. 2.3 Migration Cost and Reconfiguration Overhead-. References-. Chapter 3 Dynamic Flow Migration: A Model-Based Optimization Approach-. 3.1 Model Assumptions-. 3.1.1 M/M/1 VNF Packet Processing Queueing Model-. 3.1.2 Generalized Processor Sharing (GPS)-. 3.2 Optimization Model for Dynamic Flow Migration-. 3.3 Mixed Integer Quadratically Constrained Programming (MIQCP) Problem Transformation-. 3.3.1 Optimality Gap-. 3.3.2 Optimal Solution Mapping-. 3.4 Low-Complexity Heuristic Flow Migration Algorithm-. 3.4.1 Algorithm Overview-. 3.4.2 Redistribution of Hop Delay Bounds-. 3.4.3 Migration Decision-. 3.4.4 Iterative Resource Loading Threshold Update-. 3.4.5 Complexity Analysis-. 3.5 Simulation Results-. 3.6 Summary-. References-. Chapter 4 Dynamic VNF Resource Scaling and Migration: A Machine Learning Approach-. 4.1 Nonstationary Traffic Model-. 4.2 Machine Learning Tools for Analysis and Decision-. 4.2.1 Bayesian Conjugate Analysis-. 4.2.2 Gaussian Process Regression-. 4.2.3 Reinforcement Learning-. 4.3 Resource Demand Prediction for Dynamic VNF Resource Scaling-. 4.3.1 Bayesian Online Change Point Detection-. 4.3.2 Traffic Parameter Learning-. 4.3.3 Resource Demand Prediction-. 4.4 Deep Reinforcement Learning for Dynamic VNF Migration-. 4.4.1 MarkovDecision Process-. 4.4.2 Penalty-Aware Deep Q-Learning Algorithm-. 4.5 Simulation Results-. 4.6 Summary-. References-. Chapter 5 Dynamic VNF Scheduling for Network Utility Maximization-. 5.1 Discrete-Time VNF Packet Processing Queueing Model-. 5.1.1 Physical Packet Processing Queue-. 5.1.2 Delay-Aware Virtual Packet Processing Queue-. 5.2 Stochastic VNF Scheduling: Problem and Solution-. 5.2.1 Stochastic Problem Formulation-. 5.2.2 Lyapunov Optimization and Problem Transformation-. 5.2.3 Online Distributed Algorithm-. 5.3 VNF Scheduling with Packet Rushing-. 5.3.1 Packet Rushing Analysis-. 5.3.2 Modified VNF Scheduling Algorithm-. 5.4 Simulation Results-. 5.5 Summary-. References-. Chapter 6 Conclusions and Future Research Directions-. 6.1 Conclusions-. 6.2 Future Research Directions-. References.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
5G Networks;Dynamic Resource Management;Network Slicing;Software-Defined Networks (SDN);Network Function Virtualization (NFV);Service Function Chain (SFC);Quality-of-Service (QoS) Provisioning;Delay-Sensitive Services;Queueing Model;Optimization, Stochastic/Lyapunov Optimization;Machine Learning;software defined networking (SDN);end-to-end (E2E) delay;virtual network function (VNF);traffic dynamics;non-stationary traffic;dynamic flow migration
