Next-Generation Technologies in Cloud Computing

About the Editors xxv List of Contributors xxvii Preface xxix Acknowledgments xxxi 1 Introduction: Reimagining Cloud Computing in the Age of AI and Sustainability 1 Karan Alang and Anant Kumar 1.1 Introduction 1 1.2 The Evolution of Cloud Computing: From Mainframes to AI-Powered Everything 1 1.3 The Sustainability Imperative 7 1.4 Challenges and Opportunities 9 1.5 Conclusion 11 1.6 Future Scope 11 2 The Evolution of Cloud Computing: From Virtual Machines to Serverless 15 Anuj Ashok Potdar and Pronnoy Goswami 2.1 Introduction 15 2.2 The Definition of Cloud Computing 16 2.3 Historical Context and Significance 17 2.4 Evolution Timeline Overview 18 2.5 The Foundations: Virtual Machines and Early Virtualization 20 2.6 The Birth of Modern Cloud Computing 23 2.7 The Serverless Paradigm 26 2.8 Conclusion 27 3 Exploring Cloud-Native Microservices Architectures and Design Patterns 31 Prashanthi Matam and Venkata Naga Kartik Pidatala 3.1 Introduction 31 3.2 Fundamentals of Cloud-Native Computing 33 3.3 Microservices Architecture (MSA) Overview 36 3.4 Cloud-Native Migration Strategies to Microservices 38 3.5 Core Design Patterns in Microservices 39 3.6 Data Management Patterns 40 3.7 Event-Driven Architectures and Microservices 42 3.8 Practical Considerations and Best Practices 43 3.9 Emerging Trends and Future Directions 45 3.10 Case Studies and Real-World Applications 49 3.11 Conclusion 51 4 Multicloud and Hybrid Cloud Strategies for Resilient Infrastructure: An Observability-Driven Framework for Modern Distributed Systems 55 Aditya Gupta and Pronnoy Goswami 4.1 Introduction 55 4.2 Related Work and Theoretical Foundations 56 4.3 Multicloud Observability Architecture Framework 58 4.4 Distributed Tracing Implementation Strategy 61 4.5 Multimodal Data Fusion and Analytics 63 4.6 Cross-Cloud Security and Compliance Observability 65 4.7 Implementation Guidelines and Best Practices 67 4.8 Performance Evaluation and Verification 68 4.9 Future Directions and Emerging Technological Developments 70 4.10 Conclusion 72 5 Machine Learning as a Service (MLaaS): Enabling Scalable AI 75 Raghuram Katakam and Ashwin Prakash Nalwade 5.1 Introduction 75 5.2 The Evolution of Machine Learning 76 5.3 Frameworks and Tools in MLaaS 77 5.4 Challenges and Future Outlook 86 5.5 Conclusion 87 6 Cloud Cost Optimization and the Rise of FinOps 91 Dhivya Nagasubramanian 6.1 Introduction: Cloud Cost Complexity and the Capital Expenditures (CapEx)-to-Operating Expenditures (OpEx) Shift 91 6.2 From CapEx Comfort to OpEx Chaos 91 6.3 The Rise of FinOps: Making Cloud Spending Make Sense 92 6.4 You're Not Alone—The Data Tells the Story 92 6.5 A New Mindset for a New Era 92 6.6 The Emergence and Evolution of FinOps 94 6.7 Core FinOps Principles and Lifecycle 95 6.8 Holistic Cost Management Framework 98 6.9 Beyond Single-Workload Optimization 98 6.10 Cost-Aware Architecture and Operations 99 6.11 AI/ML for Predictive Cloud Cost Management 100 6.12 FinOps in Action: Real-World Case Studies 101 6.13 The Future of FinOps: Sustainability, Cross-Cloud Arbitrage, and Decentralized Models 104 6.14 Agentic AI Orchestration: The Next Frontier in FinOps 108 6.15 Conclusion 109 7 FinOps for AI Workloads 113 Anaranya Bagchi 7.1 Introduction 113 7.2 Types of AI Workloads and Cost Drivers 114 7.3 FinOps Principles Applied to AI 117 7.4 Challenges in FinOps for AI Workloads 125 7.5 Conclusion 126 8 AI-Driven Cloud Services and Intelligence Automation 129 Prashanthi Matam and Venkata Naga Kartik Pidatala 8.1 Introduction 129 8.2 Evolution of Cloud Computing: From Virtualization to Cloud-Native Architectures 132 8.3 Intelligent Automation in Cloud Operations 137 8.4 Emerging Paradigms: Generative and Agentic AI in Cloud Services 143 8.5 Foundations of Multimodal AI 145 8.6 Future Trends and Opportunities 147 8.7 Conclusion 148 9 AIOps: Intelligent Cloud Observability and Incident Management 151 Milankumar Rana and Jyoti Kunal Shah 9.1 Introduction 151 9.2 Background and Evolution of AIOps 152 9.3 Cloud Observability Fundamentals 154 9.4 Architecture of AIOps Platforms 156 9.5 Data Pipelines and Telemetry Management 161 9.6 AI/ML Techniques for Intelligent Observability 165 9.7 Anomaly Detection and Root Cause Analysis 169 9.8 Incident Management Workflow 172 9.9 Case Studies and Industry Implementations 175 9.10 Best Practices for AIOps Adoption 179 9.11 Challenges and Limitations 182 9.12 Future Directions in AIOps 183 9.13 Conclusion 185 10 Cloud-Native DevSecOps and Shift-Left Security Practices 187 Jay Shah and Garima Bajpai 10.1 Introduction 187 10.2 State of DevSecOps and Shift-Left Security 188 10.3 Adoption of DevSecOps 192 10.4 Implementing DevSecOps with Frameworks 192 10.5 What Is a Maturity Model? 193 10.6 Best Practices 194 10.7 Challenges and Future Outlook 196 10.8 Conclusion 197 11 Autonomous Cloud Infrastructure and Self-Healing Systems 199 Vinod Goje and Manoj Ravi 11.1 Introduction 199 11.2 Background and Context 203 11.3 Self-Healing Mechanisms and Implementation Strategies 210 11.4 Case Study: Netflix's Implementation 214 11.5 Conclusion 215 12 Serverless Computing and Event-Driven Cloud Architectures 219 Jyoti Shah and Milankumar Rana 12.1 Introduction 219 12.2 Background and Related Work 220 12.3 Challenges 224 12.4 Proposed Framework 226 12.5 Architecture Overview 229 12.6 Implementation Considerations 232 12.7 Case Study 235 12.8 Challenges and Limitations 239 12.9 Future Work 241 12.10 Conclusions 243 13 Zero Trust Architecture in Cloud Environments 247 Aparna Achanta and Vinod Goje 13.1 Introduction 247 13.2 Zero Trust in the Cloud 249 13.3 Threat Actors in the Cloud 250 13.4 How the Cloud Embraces Zero Trust 250 13.5 Zero Trust Governance for IAM 252 13.6 Micro-Segmentation for Network Control 253 13.7 Zero Trust Network Access (ZTNA) 254 13.8 How Micro-Segmentation Prevents Lateral Movement 255 13.9 The Role of Unified Endpoint Management (UEM) 255 13.10 Continuous Authentication and Session Monitoring 256 13.11 SaaS-Specific Zero Trust Strategies 257 13.12 PaaS Security Controls 257 13.13 Visibility, Logging, and Threat Detection 258 13.14 Centralized Log Aggregation Architecture 258 13.15 SIEM/SOAR Integration for Zero Trust 259 13.16 Cloud-Native Threat Detection Services 260 13.17 Data-Centric Security 260 13.18 Conclusions 261 13.19 Future Work 262 14 Predictive Risk Intelligence and Governance Framework in Multicloud Environments 265 Priya Ranjani Mohan and Yugandhar Suthari 14.1 Introduction: From Reactive to Predictive 265 14.2 Core Challenges in Multicloud Governance 266 14.3 PRIG Framework Architecture and Components 268 14.4 Building Your Organization's PRIG Infrastructure 270 14.5 Out-of-the-Box Tools for Predictive Decisions 271 14.6 Building Custom ML Models and Risk Prediction 273 14.7 Making Predictive Decisions and Taking Action 274 14.8 The Glue That Holds It Together 276 14.9 Considerations for Potential Issues When Implementing PRIG Framework 277 14.10 Real-World Case Studies 277 14.11 Outlook and Recommendations 279 14.12 Conclusion 281 15 Security and Compliance for Cloud-Native Applications 283 Vaishnavi Gudur and Ashish Kattamuri 15.1 Introduction 283 15.2 Background/Context 284 15.3 Core Content 287 15.4 Challenges 292 15.5 Future Outlook 294 15.6 Conclusion 296 16 Data Privacy, Sovereignty, and Cloud Localization Laws 299 Dhivya Nagasubramanian and Kiran Kumar Reddy Puram 16.1 Introduction: When the Cloud Hits the Ground 299 16.2 Global Trends in Data Privacy and Localization 300 16.3 Regional Regulatory Landscape 301 16.4 Industry Case Studies: Impact of Sovereignty Requirements 306 16.5 Architecting for Compliance: Technical Approaches to Sovereignty 309 16.6 Evolution and Future Outlook 313 16.7 Conclusion 315 17 Sustainable Cloud Computing and Carbon-Aware Architectures 319 Vamsi Alla and Ashish Kattamuri 17.1 Introduction 319 17.2 Evolution of Cloud Computing: The Foundation for Sustainability 321 17.3 Principles of Sustainable Cloud Computing 322 17.4 Core Frameworks for Sustainable Cloud Computing 325 17.5 Use Cases and Industry Relevance 332 17.6 Difficulties and Future Vision 334 17.7 Sustainable Cloud Computing's Prospect 337 17.8 Conclusion 338 18 Quantum Computing in the Cloud: Opportunities and Challenges 341 Ashwin Prakash Nalwade and Khan Shariya Hasan Upoma 18.1 Introduction 341 18.2 Background 342 18.3 Quantum Computing in the Cloud 345 18.4 Quantum Computing—Key Strengths 350 18.5 Challenges and the Future 351 18.6 Conclusion 353 19 Cloud Platforms for Scientific Research and HPC Workloads 355 Anant Kumar 19.1 Introduction 355 19.2 Background and Context 356 19.3 Core Content: Frameworks, Use Cases, and Technical Depth 358 19.4 Container Orchestration for Scientific Workloads 360 19.5 Workflow Management Systems 361 19.6 Challenges and Future Outlook 365 19.7 Emerging Trends 366 19.8 Conclusion 369 20 Ethics, Bias, and Responsible AI in Cloud Environments 373 Sreekanth B. Narayan and Karan Alang 20.1 Introduction 373 20.2 Key Ethical Principles 375 20.3 Bias in AI 377 20.4 Responsible AI Practices 379 20.5 Implementation Considerations and Good Practices 382 20.6 Cloud Environments and AI 383 20.7 Case Studies 385 20.8 Future Directions 388 21 Conclusion—The Future Cloud: Ethical, Autonomous, and Planet-Aware 393 Pronnoy Goswami and Aditya Gupta 21.1 Introduction 393 21.2 The Enduring Arc of Abstraction and Its Unseen Costs 394 21.3 From Monitoring Silos to Multicloud Operational Resilience 395 21.4 The Challenge of Governance-Aware Autonomy 397 21.5 From Optimization to Obligation: The Rise of Ethical and Planet-Aware Architectures 398 21.6 Redefining the Economics: Financial Operations, Sovereignty, and Zero Trust 400 21.7 Synthesis and a Forward-Looking Research Agenda 402 21.8 Conclusion and Future Scope 403 References 403 Glossary 405 Index 407