Description
Comprehensive exploration of emerging cloud computing technologies, focusing on generative AI, cloud security, sustainable computing practices, and edge computing
Next-Generation Technologies in Cloud Computing delves into the development and future of cloud ecosystems, highlighting key technological milestones. Unlike traditional cloud computing books, this volume uniquely integrates AI, cybersecurity, sustainability, and edge computing into a single comprehensive resource. It explores the latest advancements, from generative AI and quantum computing to zero-trust security and green cloud practices, making it a forward-looking guide for readers of all backgrounds.
The book bridges theory and practice by including case studies from industries like healthcare, finance, and IoT, showcasing how cloud innovations are transforming real-world applications. Contributions from leading academics, researchers, and industry experts provide valuable perspectives on deploying next-generation cloud solutions.
Rather than focusing solely on performance and scalability, this volume emphasizes eco-friendly cloud solutions and the ethical implications of AI-driven cloud systems. It highlights strategies for achieving carbon-neutral cloud infrastructures and securing AI applications responsibly, addressing the growing demand for sustainable and ethical technology practices.
Next-Generation Technologies in Cloud Computing includes information on:
- Machine Learning as a Service (MLaaS) and its advantages for businesses and developers, emphasizing multi-cloud optimization
- Edge computing’s role in enhancing real-time data processing, particularly in IoT and 5G networks
- Eco-friendly cybersecurity and AI-powered threat detection
- Privacy-preserving techniques, innovations in IoT platforms, and cost optimization for cloud AI
- Regulatory frameworks including the EU AI Act, the NIST AI Risk Management Framework, OECD AI Principles, and U.S. Executive Orders on AI
Next-Generation Technologies in Cloud Computing is an essential resource on the subject for cloud professionals, cybersecurity experts, AI researchers, students, educators, policymakers, and anyone interested in understanding the future of cloud technology.
Table of Contents
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
