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{ "item_title" : "Bringing Machine Learning to Software-Defined Networks", "item_author" : [" Zehua Guo "], "item_description" : "1 Machine Learning for Software-Defined Networking1.1 Introduction of Software-Defined Networking1.1.1 Software-Defined Wide Area Network1.1.2 Software-Defined Data Center Networks1.2 Introduction of Machine Learning Techniques1.2.1 Deep Reinforcement Learning1.2.2 Multi-Agent Reinforcement Learning1.2.3 Graph Neural Network2 Deep Reinforcement Learning-based Traffic Engineering in SD-WANs2.1 Introduction of Traffic Engineering2.2 Motivation2.2.1 Problems of Existing Solutions2.2.2 Opportunity2.3 Overview of ScaleDRL2.4 Design Details of ScaleDRL2.4.1 Pinning Control in the Offline Phase2.4.1.1 Pinning Control2.4.1.2 Link Selection Algorithm2.4.2 DRL Implementation of the Online Phase2.4.2.1 DRL Framework2.4.2.2 Customization of Neural Networks and Interfaces2.5 Performance Evaluation2.5.1 Simulation Setup2.5.2 Comparison Scheme2.5.3 Simulation Results2.6 Conclusion3 Multi-Agent Reinforcement Learning-based Controller Load Balancing in SD-WANs3.1 Introduction of Controller Load Balancing3.2 Motivation3.2.1 Problems of Existing Solutions3.2.2 Opportunity3.3 Controller Load Balancing Problem Formulation2.3.1 Control Plane Resource Utilization Modeling2.3.2 Control Plane Load Balancing Problem Formulation2.3.3 Problem Complexity Analysis3.4 Overview of MARVEL3.5 Design Details of MARVEL3.5.1 Training Phase3.5.2 Working Phase3.5.3 MARVEL Model Implementation3.6 Performance Evaluation3.6.1 Simulation Setup3.6.2 Comparison Scheme3.6.3 Simulation Results3.7 Conclusion4 Deep Reinforcement Learning-based Flow Scheduling for Power Efficiency in Data Center Networks4.1 Introduction of Data Center Networks4.1.1 Traffic Classification4.1.2 Traffic Dynamic Analysis4.2 Motivation4.2.1 Problems of Existing Solutions4.2.2 Opportunity4.3 Problem formulation4.3.1 Design Consi", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/81/194/873/9811948739_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Bringing Machine Learning to Software-Defined Networks|Zehua Guo

Bringing Machine Learning to Software-Defined Networks

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Overview

1 Machine Learning for Software-Defined Networking

1.1 Introduction of Software-Defined Networking

1.1.1 Software-Defined Wide Area Network

1.1.2 Software-Defined Data Center Networks

1.2 Introduction of Machine Learning Techniques

1.2.1 Deep Reinforcement Learning

1.2.2 Multi-Agent Reinforcement Learning

1.2.3 Graph Neural Network

2 Deep Reinforcement Learning-based Traffic Engineering in SD-WANs

2.1 Introduction of Traffic Engineering

2.2 Motivation

2.2.1 Problems of Existing Solutions

2.2.2 Opportunity

2.3 Overview of ScaleDRL

2.4 Design Details of ScaleDRL

2.4.1 Pinning Control in the Offline Phase

2.4.1.1 Pinning Control

2.4.1.2 Link Selection Algorithm

2.4.2 DRL Implementation of the Online Phase

2.4.2.1 DRL Framework

2.4.2.2 Customization of Neural Networks and Interfaces

2.5 Performance Evaluation

2.5.1 Simulation Setup

2.5.2 Comparison Scheme

2.5.3 Simulation Results

2.6 Conclusion

3 Multi-Agent Reinforcement Learning-based Controller Load Balancing in SD-WANs

3.1 Introduction of Controller Load Balancing

3.2 Motivation

3.2.1 Problems of Existing Solutions

3.2.2 Opportunity

3.3 Controller Load Balancing Problem Formulation

2.3.1 Control Plane Resource Utilization Modeling

2.3.2 Control Plane Load Balancing Problem Formulation

2.3.3 Problem Complexity Analysis

3.4 Overview of MARVEL

3.5 Design Details of MARVEL

3.5.1 Training Phase

3.5.2 Working Phase

3.5.3 MARVEL Model Implementation

3.6 Performance Evaluation

3.6.1 Simulation Setup

3.6.2 Comparison Scheme

3.6.3 Simulation Results

3.7 Conclusion

4 Deep Reinforcement Learning-based Flow Scheduling for Power Efficiency in Data Center Networks

4.1 Introduction of Data Center Networks

4.1.1 Traffic Classification

4.1.2 Traffic Dynamic Analysis

4.2 Motivation

4.2.1 Problems of Existing Solutions

4.2.2 Opportunity

4.3 Problem formulation

4.3.1 Design Consi

This item is Non-Returnable

Details

  • ISBN-13: 9789811948732
  • ISBN-10: 9811948739
  • Publisher: Springer
  • Publish Date: October 2022
  • Dimensions: 9.21 x 6.14 x 0.17 inches
  • Shipping Weight: 0.29 pounds
  • Page Count: 68

Related Categories

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