Autonomous Networks: Using AI to Advance Operations in SP and Enterprise Domains

Description

Autonomous Networks: Using AI to Advance Operations in SP and Enterprise Domains is a practical, forward-looking guide to the next generation of network operationswhere AI, automation, observability, and closed-loop control enable networks to operate with greater speed, scale, and resilience while reducing manual intervention.

As enterprise and service provider environments become increasingly complex, traditional reactive operations can no longer keep pace. This book explains how autonomous networking combines model-driven telemetry, distributed tracing, AIOps, automation frameworks, MLOps, and IT service management integration to create intelligent systems capable of sensing conditions, making decisions, and executing actions in real time.

Written for network engineers, architects, operations teams, and technology leaders, this vendor-agnostic guide provides a clear roadmap for evolving from basic automation initiatives to fully autonomous network operations. Readers will learn how to implement AI-driven operational models across both enterprise and service provider environments while addressing security, governance, compliance, and organizational transformation.

Through real-world case studies, architectural patterns, and industry-aligned frameworks, the authors demonstrate how narrow AI, generative AI, closed-loop automation, and predictive analytics can improve network reliability, operational efficiency, and service assurance at scale.

Whether you are modernizing existing infrastructure or building next-generation intelligent networks, this book delivers actionable strategies for designing, operating, and securing autonomous networking environments in an increasingly AI-driven world.

Sample Content

Table of Contents

    Introduction xxiii

Chapter 1 Network Autonomy 1

    Introduction 1

    State of Network Automation Today 2

    Industry Frameworks and Trends 27

    Challenges in Achieving Autonomy 39

    People and Processes 45

    System Integrations 53

    Summary and Future Considerations 58

Chapter 2 Regulations Impact on Autonomous Networks 59

    Introduction 59

    Short History of Data Privacy 59

    Overview of EU Digital Regulations 72

    The State of AI Regulation in the Rest of the World 80

    How Companies React to Existing AI and Data Regulation 88

    Regulation Impact on Data and AI Projects and Mitigation Measures 96

    Summary and Future Considerations 109

    References 110

Chapter 3 Observability and Data Sources 115

    Introduction 115

    Network Data Sources 117

    Model-Driven Telemetry 131

    Observability and Tracing Principles 135

    Audit Trails in Autonomous Networks 147

    Collecting and Correlating Disparate Traces 150

    Network Observability Architecture Patterns 154

    Observability Use Cases 156

    Summary and Future Considerations 165

    References 165

Chapter 4 Conventional and Task-Specific AI/ML Models Usage for Autonomous Networks 167

    Introduction 167

    Short History of Artificial Intelligence 168

    Machine Learning Overview 172

    Semi-Supervised Learning 184

    Unsupervised Learning 186

    Reinforcement Learning 191

    Deep Learning and Neural Networks 196

    Model Evaluation, Validation, and Monitoring Metrics 206

    Summary and Future Considerations 219

    References 220

Chapter 5 Generative AI in the Context of Autonomous Networks 221

    Introduction 221

    Agentic Workflows 252

    Federated Learning 264

    Infrastructure for xLMs 268

    Networking Use Cases 276

    Summary and Future Considerations 279

    References 280

Chapter 6 Automated Execution and Testing 281

    Introduction 281

    Industry Automation Frameworks and Standards 284

    Automation Tools 287

    Cisco Virtual Kubelet 314

    Network Test Automation 315

    Domain vs. Cross-Domain Automation Architecture 334

    Network Automation Use Cases 337

    Summary and Future Considerations 343

    References 343

Chapter 7 Closed-Loop Command and Control 345

    Introduction to Closed-Loop Systems 345

    Evolution of Network Operations 351

    Core Concepts of Closed-Loop Network Operations 360

    Closed-Loop Operations Architectural Framework 371

    High-Value Use Cases in Autonomous Network Operations 384

    Challenges and Limitations in Implementing Closed-Loop Operations 395

    Closed-Loop Future Evolution 397

    Summary and Future Considerations 398

    References 399

Chapter 8 Communication Service Provider Autonomous Network Vision 401

    Background and Industry Context 401

    End-to-End Autonomous Network Architecture 411

    Typical Use-Case Deployment for Level 3+ 428

    Typical Use Case Deployment for Level 4+ 435

    Business Benefits 449

    Summary and Future Considerations 455

    References 455

Chapter 9 Enterprise Domain Vision 459

    Introduction 459

    The Function of Todays Networks 464

    Usage Ratios for Artificial Intelligence Methods 466

    Security Considerations 467

    End-to-End Autonomous Network Evolution Roadmap 484

    Alignment with Core Business Applications 489

    Planning and Deployment 494

    Integration with Existing Systems 495

    Technical Challenges 499

    Summary and Future Considerations 506

    References 507

Chapter 10 Autonomous Network ConsiderationsOrganizational Change 509

    Short History of Network Operations 509

    Core Network Operation Concepts and Frameworks 516

    Typical Network Operations Center Setup for CSP 540

    Typical NOC Setup for an IT Enterprise 548

    Organizational Culture Shift 553

    Key Skills Required for AN-Based Operations 560

    Summary and Future Considerations 572

    References 573

TOC, 9780135473368, 7/13/26

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