Cloud Native 5g Architecting Telecom With Kubernetes And Ai 2025
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Cloud-Native 5G: Architecting Telecom with Kubernetes and AI 2025
Author: Author:1- Veeresh Nunavath, uthor:2- Dr. Lalit Kumar
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE In recent years, the telecommunications industry has witnessed a profound transformation, driven by the convergence of cloud-native paradigms, container orchestration, and artificial intelligence. “Cloud-Native 5G: Architecting Telecom with Kubernetes and AI” is born out of the recognition that tomorrow’s networks must be agile, scalable, and intelligent enough to meet ever-evolving demands—from ultralow-latency industrial automation to immersive augmented-reality experiences. Rather than simply applying legacy architectures to next-generation radio access and core networks, we now have the opportunity to reimagine every layer of the telecom stack through cloud-native principles: microservices, declarative APIs, immutable infrastructure, continuous delivery, and observability. This book is intended as both a roadmap and a hands-on guide for network architects, DevOps engineers, AI practitioners, and decision-makers who are charting a course toward fully software-defined, self-optimizing 5G systems. We begin by exploring the fundamental motivations for cloud-native 5G: the need to accelerate time-to-market, dynamically scale resources, optimize capital and operational expenditures, and inject predictive intelligence into network operations. From there, we delve into the core building blocks of a cloud-native telecom stack: Kubernetes as the orchestration substrate, service meshes for secure and resilient communications, and CI/CD pipelines to automate deployments across distributed edge and regional clusters. Artificial intelligence sits at the heart of the transformation. In early chapters, we introduce machine-learning workflows tailored for network analytics—how to collect, cleanse, and label telemetry at scale; how to train and validate models that predict load, detect anomalies, and anticipate faults; and how to deploy inference engines as lightweight microservices at the network edge. Later sections demonstrate how reinforcement learning and closed-loop control can enable networks to self-tune parameters—orchestrating spectrum allocation, beamforming, and load-balancing in real time—while minimizing human intervention. As the reader progresses, use cases will bring theory to life: implementing cloud-native network slicing to guarantee differentiated SLAs across diverse verticals; leveraging AI-driven traffic steering to optimize quality-of-experience in mobile broadband and ultra-reliable low-latency contexts; and integrating open-source 5G cores with commercial telco cloud platforms. Each chapter balances conceptual clarity with code snippets, architectural diagrams, and references to leading projects—offering practical guidance whether you’re evaluating CNCF-backed network function virtualization (NFV) frameworks or building proprietary AI pipelines. We also address organizational considerations: evolving teams toward DevTelco mindsets, redefining release management and security practices for containerized network functions, and fostering collaboration between data scientists, software engineers, and network operators. The book concludes with a forward-looking perspective on 6G trends—how terahertz communications, distributed intelligence, and digital twins may further disrupt the telecom landscape—and where open-source communities can drive innovation. Whether you’re an engineer tasked with migrating legacy network functions into Kubernetes clusters, a researcher designing novel AI-based management algorithms, or an executive charting your company’s strategic roadmap, this volume aims to equip you with both the conceptual frameworks and the hands-on techniques necessary to architect resilient, scalable, and intelligent 5G systems. The journey toward cloud-native telco is complex—but with Kubernetes and AI as your allies, it is also the most exciting frontier in digital infrastructure today. Authors
Cloud-Native 5G Networks: Design, Deployment, and Optimization in AWS 2025
Author: AUTHOR:1- JayavelanJayabalan, AUTHOR:2-Prof (Dr) Shubhranshu Vikram Singh
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE The global telecommunications landscape is undergoing a monumental transformation, driven by the convergence of cloud computing and 5G networks. As industries race toward hyperconnectivity and ultra-low latency, the fusion of cloud-native principles with next-generation mobile networks is no longer a future ambition—it is today’s imperative. Cloud-Native 5G Networks: Design, Deployment, and Optimization in AWS emerges in this critical juncture to guide technologists, architects, and decision-makers in understanding and implementing scalable, resilient, and intelligent 5G systems using Amazon Web Services (AWS). This book was conceived from the realization that deploying 5G networks in a cloud-native paradigm requires more than infrastructure knowledge—it demands an integrated understanding of telecom architecture, DevOps automation, security, and edge computing, all optimized within the capabilities of AWS’s globally distributed environment. Bridging these domains is both a technical and strategic necessity, and our goal with this volume is to demystify that intersection for readers across industries. Spanning twelve meticulously curated chapters, this book journeys from foundational principles to forward-looking trends. We begin by grounding readers in the vision and driving forces behind cloud-native 5G on AWS. We then introduce the architectural building blocks that define modern 5G systems and map them to AWS regions, availability zones, Wavelength zones, and edge locations. The chapters delve into real-world methodologies for containerizing network functions with Docker, Kubernetes, and Amazon EKS, and explore edge deployments through AWS Outposts, Wavelength, and the Snow Family. Critical operational topics such as infrastructure as code, CI/CD pipelines, and service mesh architecture are detailed using AWS-native tools like CloudFormation, CDK, Code Pipeline, and App Mesh. We place a strong emphasis on end-to-end security, compliance, and observability, equipping readers to build zero-trust environments with full visibility using CloudWatch, X-Ray, and Prometheus. In our concluding chapters, we look ahead to the transformative role of AI/ML in self-optimizing networks and offer insights into preparing for 6G innovations and next-gen edge computing frameworks. This trajectory ensures that readers not only master the current state of cloud-native 5G but are also poised to lead in shaping its evolution. We thank the broader cloud and telecom communities for inspiring this work. It is our hope that this book fuels innovation, drives knowledge-sharing, and accelerates the democratization of next-gen connectivity solutions across the globe. JayavelanJayabalan Prof. (Dr) Shubhranshu Vikram Singh May 2025
AI-Driven Networks: Architecting the Future of Autonomous, Secure, and Cloud-Native connectivity 2025
Author: AUTHOR:1-DIPESH JAGDISH KASHIV, AUTHOR:2-PROF (DR) MOPARTHI NAGESWARA RAO
language: en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date:
PREFACE In an age defined by relentless digital innovation, networks have evolved far beyond simple conduits for data. They now serve as the critical nervous system of entire industries—powering everything from real-time financial transactions to massive Internet-of-Things deployments and immersive 5G applications. Yet the exponential growth in traffic volumes, the dynamic nature of modern applications, and the sophistication of cyber-threats have exposed the limitations of static, manually managed infrastructures. AI-Driven Networks: Architecting the Future of Autonomous, Secure, and Cloud-Native Connectivity was conceived to meet this challenge head-on, providing a comprehensive roadmap for embedding intelligence, resilience, and automation into every layer of the network stack. Our journey begins in Chapter 1: Foundations of AI-Driven Networking, where we introduce the core principles that underpin the fusion of artificial intelligence and networking. After grounding readers in key machine-learning paradigms—supervised, unsupervised, and reinforcement learning—we map these techniques onto fundamental networking functions such as routing, traffic classification, and anomaly detection. Building on these fundamentals, Chapter 2: Intent-Based and Self-Driving Architectures explores how high-level business objectives can be translated into automated network behaviors. By examining intent interfaces—ranging from declarative APIs to natural-language processing tools—we demonstrate how directives like “ensure sub-5 ms latency between our core data centers” can be codified, deployed, and continuously enforced across software-defined networking controllers, routers, and security gateways. In Chapter 3: Data-Plane Intelligence—From Telemetry to Insights, we dive into the lifeblood of AI-driven networks: data. Modern network devices emit rich, high-velocity telemetry streams—flow records, per-queue latency histograms, packet-level metrics—and ingesting, storing, and analyzing this data at scale is a monumental engineering challenge. We detail scalable architectures for real-time telemetry collection, explore unsupervised anomaly-detection models that surface emerging congestion hotspots, and show how predictive analytics can forecast capacity needs hours or days in advance to enable proactive resource scaling. Chapter 4: Control-Plane Optimization with Reinforcement introduces reinforcement learning as the engine for adaptive, closed-loop control. Beginning with tabular Q-Learning methods that dynamically tune link weights in OSPF and segment-routing protocols, we progress to advanced policy-gradient algorithms—REINFORCE and actor-critic variants—that learn to split flows optimally for throughput and fairness. Multi-agent RL scenarios illustrate how multiple controllers, or administrative domains can cooperate or compete to maximize global efficiency, all while honoring strict service-level agreements. Security is woven throughout every chapter, but Chapter 5: Secure by Design—AI for Threat Detection and Response provides an in-depth exploration of zero-trust enforcement and AI-driven defenses. We unpack the “never trust, always verify” paradigm, showing how continuous authentication—powered by behavioral profiling, device-fingerprinting, and contextual risk scoring—can prevent unauthorized lateral movement even after perimeter breaches. AI-based micro-segmentation adapts dynamically to traffic patterns, while deep-learning models detect novel attack vectors. We conclude with frameworks for automated incident response, orchestrating containment actions like host isolation, firewall rule updates, and credential rotations in real time. As networks embrace containerization and cloud-native platforms, Chapter 6: Cloud-Native and Kubernetes Integration examines how microservices design patterns, service meshes, and GitOps workflows can host AI inference engines for fine-grained policy enforcement. We show how Kubernetes CNI plugins incorporate ML models for per-pod traffic classification, how canary deployments can be orchestrated through AI-driven traffic-splitting strategies, and how declarative pipelines ensure safe, auditable policy roll-outs. Subsequent chapters synthesize these advancements into end-to-end automation and observability frameworks (Chapters 7–9), explore the unique opportunities at the network edge and in 5G environments (Chapter 10), peer into the future with quantum networking and post-quantum resilience strategies (Chapter 11), and address the governance, compliance, and ethical considerations that accompany the adoption of autonomous, AI-driven networks (Chapter 12). Whether you are a network architect designing carrier-grade backbones, a security engineer safeguarding mission-critical infrastructure, or a researcher advancing autonomous systems, this book equips you with the theories, tools, and real-world techniques needed to build networks that not only meet today’s demands but also learn, adapt, and scale as the digital landscape evolves. The future of connectivity is intelligent—and it starts here. Authors Dipesh Jagdish Kashiv