An Introduction to the Machine Learning Empowered Intelligent Data Center Networking
Fundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks.
Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security.Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.
Author(s): Ting Wang, Bo Li, Mingsong Chen, Shui Yu
Series: SpringerBriefs in Computer Science
Publisher: Springer
Year: 2023
Language: English
Pages: 122
City: Singapore
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
