Machine Learning Empowered Intelligent Data Center Networking. Evolution, Challenges and Opportunities

Description

Preface Acknowledgments Contents Acronyms 1 Introduction References 2 Fundamentals of Machine Learning in Data Center Networks 2.1 Learning Paradigm 2.2 Data Collection and Processing 2.2.1 Data Collection Scenarios 2.2.2 Data Collection Techniques 2.2.3 Feature Engineering 2.2.4 Challenges and Insights 2.3 Performance Evaluation of ML-Based Solutions in DCN References 3 Machine Learning Empowered Intelligent Data Center Networking 3.1 Flow Prediction 3.1.1 Temporal-Dependent Modeling 3.1.2 Spatial-Dependent Modeling 3.1.3 Discussion and Insights 3.2 Flow Classification 3.2.1 Supervised Learning-Based Flow Classification 3.2.2 Unsupervised Learning-Based Flow Classification 3.2.3 Deep Learning-Based Flow Classification 3.2.4 Reinforcement Learning-Based Flow Classification 3.2.5 Discussion and Insights 3.3 Load Balancing 3.3.1 Traditional Solutions 3.3.2 Machine Learning-Based Solutions 3.3.3 Discussion and Insights 3.4 Resource Management 3.4.1 Task-Oriented Resource Management 3.4.2 Virtual Entities-Oriented Resource Management 3.4.3 QoS-Oriented Resource Management 3.4.4 Resource Prediction-Oriented Resource Management 3.4.5 Resource Utilization-Oriented Resource Management 3.4.6 Discussion and Insights 3.5 Energy Management 3.5.1 Server Level 3.5.2 Network Level 3.5.3 Data Center Level 3.5.4 Discussion and Insights 3.6 Routing Optimization 3.6.1 Intra-DC Routing Optimization 3.6.2 Inter-DC Routing Optimization 3.6.3 Discussion and Insights 3.7 Congestion Control 3.7.1 Centralized Congestion Control 3.7.2 Distributed Congestion Control 3.7.3 Discussion and Insights 3.8 Fault Management 3.8.1 Fault Prediction 3.8.2 Fault Detection 3.8.3 Fault Location 3.8.4 Fault Self-Healing 3.8.5 Discussion and Insights 3.9 Network Security 3.10 New Intelligent Networking Concepts 3.10.1 Intent-Driven Network 3.10.2 Knowledge-Defined Network 3.10.3 Self-Driving Network 3.10.4 Intent-Based Network (Gartner) 3.10.5 Intent-Based Network (Cisco) References 4 Insights, Challenges and Opportunities 4.1 Industry Standards 4.1.1 Network Intelligence Quantification Standards 4.1.2 Data Quality Assessment Standards 4.2 Model Design 4.2.1 Intelligent Resource Allocation Mechanism 4.2.2 Inter-DC Intelligent Collaborative Optimization Mechanism 4.2.3 Adaptive Feature Engineering 4.2.4 Intelligent Model Selection Mechanism 4.3 Network Transmission 4.4 Network Visualization References 5 Conclusion Index