Platform Engineering

Concepts, Challenges and Applications
1. Auflage 2026
ISBN: 978-1-394-39588-0
Verlag: John Wiley & Sons Inc

Buch, Englisch, 512 Seiten

Platform Engineering: Concepts, Challenges and Applications

Stay at the leading edge of modern infrastructure with this definitive guide to integrating AI, blockchain, and cloud-native methodologies into robust and secure software platforms.

As enterprises across industries adopt cloud-native technologies, DevOps practices, AI-driven tools, and secure infrastructure models, platform engineering has become essential to streamlining development workflows, enhancing developer experience, and improving operational efficiency. This book is a comprehensive and timely exploration of the emerging discipline that is redefining how modern software systems are designed, developed, deployed, and scaled.

This edited volume brings together contributions from leading researchers and practitioners to offer a rich blend of theoretical foundations, design strategies, implementation methodologies, and real-world use cases. Covering a wide array of topics—from developer productivity, scalable platform design, and DevOps transformation to advanced technologies like deep learning, reinforcement learning, blockchain, and IoT—the book addresses both the opportunities and challenges involved in building robust, reusable, and secure software platforms. By presenting a structured and insightful view into this rapidly growing field, the book not only serves as a reference guide but also as a source of inspiration for innovation in platform design and deployment across diverse sectors.

Audience
Software engineers, DevOps specialists, platform engineers, cloud architects, security experts, technology leaders, and data scientists mastering modern software delivery, leveraging artificial intelligence and machine learning for building scalable and secure systems.

Autoren/Hrsg.

Kumar, K. Suresh

Sundaresan, S.

Prithiviraj, R.

Kumar, T. Ananth

Balamurugan, S.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Industrielle Qualitätskontrolle
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

Series Preface xxi
Preface xxiii
Acknowledgments xxvii

Part I: Core Concepts and Evolution of Platform Engineering 1

1 Empowering Developer Productivity through Platform Engineering: A Transformative Approach to Scalable and Streamlined Software Development 3
C. Saranya Jothi, E. Surya, M. Syed Rabiya, B. Lalitha and R. Roselinkiruba

1.1 Introduction to Platform Engineering 4
1.2 Core Components of a Platform Engineering Strategy 6
1.3 Automation and Its Role in Enhancing Productivity 8
1.4 Impact on Developer Experience 11
1.5 Quantifying Developer Productivity through Platform Engineering 12
1.6 Future Trends and Opportunities in Platform Engineering 14
1.7 Conclusion 15

2 Best Practices for Building Scalable Software Platforms 19
Swetha S. and Joe Prathap P. M.

2.1 Introduction 19
2.2 Goals and Expectations 20
2.3 Basic Key Metrics 22
2.4 Cloud Computing as Infrastructure 24
2.5 Microservice Architecture 25
2.6 Choosing the Right Database Solution 26
2.7 Effective Scaling Methods 28
2.8 Conclusion 30

3 The Role of Platform Engineering in DevOps Transformation—The Future of Software Delivery: How Platform Engineering Transforms DevOps 33
S. Sri Devi, T. Sangeetha and Olukayode A. Oki

3.1 Introduction 34
3.2 Benefits of Platform Engineering 36
3.3 Role and Responsibilities of Platform Engineers 37
3.4 Tools/Technology in Platform Engineering 37Contents vii
3.5 The Origin Story of DevOps 38
3.6 Extending Agile to the Full Life Cycle 39
3.7 Difference Between Software Engineer and DevOps Engineer 40
3.8 The Role of SDLC in DevOps 42
3.9 Top Programming and Scripting Languages for DevOps 44
3.10 Choosing the Optimal Operating System for DevOps: Linux, Windows, or MacOS 45
3.11 What is Command Line Interface? 46
3.12 What is a Shell? 47
3.13 Networking and Its Role in DevOps 48
3.14 Exploring Infrastructure as Code (IaC): Automation, Scalability, and Efficiency in DevOps 51
3.15 Conclusion 55

4 The Impact of Platform Engineering on Developer Productivity 59
K. Dhivya and Matthew Olusegun Adigun

4.1 Introduction 59
4.2 Key Principles and Components 65
4.3 Comparison with Platform Engineering, Traditional DevOps, and SRE 68viii Contents
4.4 Platform Engineering Centralizes Tooling and Infrastructure 71
4.5 Developer Productivity: Key Metrics and Challenges 75
4.6 Common Productivity Bottlenecks in Software Development 78
4.7 Boosting Developer Productivity 82
4.8 Self-Service Developer Portals and Internal Platforms 83
4.9 Examples of Companies that Successfully Adopted Platform Engineering 87
4.10 Proposed Project: "Boosting Developer Productivity through Platform Engineering: A Practical Exploration with Internal Developer Platforms" 90
4.11 Conclusion 94

Part II: Platform Engineering for Specific Technologies and Architectures 97

5 Platform Engineering for Cloud-Native Applications 99
Amanpreet Singh, Rupinder Singh and Jaswinder Singh

5.1 Introduction 100
5.2 Cloud-Native Applications 101
5.3 Literature Review 103
5.4 Cloud-Native Application Advantages 112
5.5 Tools and Technologies 115
5.6 Areas of Challenge in Platform Engineering 119
5.7 Challenges and Considerations 121
5.8 Conclusion 128

6 Platform Engineering for Cloud-Native Applications: Strategies for Scalable, Cost-Effective, and Automated Cloud Adoption 133
P. Divya, R. Parthiban, S. Jayalakshmi and R. Rajmohan

6.1 Introduction 134
6.2 Core Principles of Platform Engineering 134
6.3 Programming for Productivity and Networking 137
6.4 Continuous Integration and Continuous Deployment (CI/CD) 138\
6.5 Containerization and Orchestration 139x Contents
6.6 Cloud-Native Platform Stack 141
6.7 Protocols in Building and Managing Cloud-Native Platform 145
6.8 Tools and Technology in Platform Engineering 147
6.9 Challenges in Platform Engineering for Cloud-Native Applications 151
6.10 Best Practices in Platform Engineering for Cloud-Native Applications 154
6.11 Performance Analysis 155
6.12 Future Trends 157
6.13 Conclusion 158

7 Optimization Techniques Hybridized into Deep Learning Models 161
K. Pathmapriya and Joe Prathap P. M.

7.1 Introduction 162
7.2 Research Prospects 163
7.3 Deep Learning Technique for Diagnosis of Syndrome 168
7.4 Meta-Heuristics Algorihms in Medical Diagnosis 170
7.5 Data Synthesis 177
7.6 Deploying Healthcare Solutions 179
7.7 Discussion and Result 180
7.8 Challenges and Future Direction 181
7.9 Conclusion and Future Research Ideas 182

8 Machine Learning and Automation in Platform Engineering: Transforming Monitoring, Scaling, and Self-Healing 189
R. Roselinkiruba, Vasumathy M., J. Jude Moses Anto Devakanth, C. Saranya Jothi, J. Kavitha and L. Sharmila

8.1 Introduction 190Contents xi
8.2 Proposed Methodology 194
8.3 Case Study and Practical Examples 201
8.4 Experimental Results and Analysis 203
8.5 Conclusion and Future Work 210

9 An Investigative Analysis on Security Challenges in Cloud Computing Models and Solutions 215
N.A. Natraj, B. Sundaravadivazhagan, Giri. G. Hallur and Supriya Shrikant Laykar

9.1 Introduction 216
9.2 Literature Review 220
9.3 Research Methodology 224
9.4 Security Challenges and Solutions in Cloud Computing Models 233
9.5 Quantitative Analysis and Findings 244
9.6 Conclusion 250

10 Applying Ensemble Deep Learning for Enhanced Security in Platform Engineering 255
R. Saranya, S.S. Uma, T.S. Sivarani, Naveena A. Priyadharsini and Sunday Adeola Ajagbe

10.1 Introduction 256
10.2 Related Works 258
10.3 Proposed Methodology 261
10.4 Experimental Result and Discussion 272
10.5 Conclusion and Future Work 282

Part III: Application-Specific Platforms and Emerging Technologies 287

11 Reinforcement Learning–Driven Secure and Energy-Efficient Transmission Framework for Scalable Platform Engineering 289
Femila. L., S.P. Subotha, Lavanya Devi. N. and J. Arul King

11.1 Overview 290
11.2 Background 294
11.3 Approach/Methodology 298
11.4 Results and Discussion 303
11.5 Conclusion 306

12 Platform Engineering for Scalable AI Deployments in Healthcare: Enabling Automated Skin Blemish Detection 309
P. Kalpana, T. Sangeetha, S. Siamala Devi and Morenikeji E. Coker

12.1 Introduction 310
12.2 Related Work 313
12.3 Modules 315
12.4 Methodology 318
12.5 Experiments 324
12.6 Performance Analysis 328
12.7 Conclusion 331

13 AI-Driven Platform Engineering for Skin Disease Diagnosis: A Comparative Study of DenseNet Architectures 333
R. Karthick Manoj, S. Aasha Nandhini and M. Batumalay

13.1 Introduction 334
13.2 Literature Survey 335
13.3 Methodology 339
13.4 Result and Discussion 343
13.5 Conclusion 350

14 An Overview of Current Advances in Blockchain Technology, Platform Engineering, and DevOps and Their Implications 353
Balaji Ganesh R., Deebalakshmi R. and R. Thilagavathy

14.1 Introduction 354
14.2 Literature Review 355
14.3 Characteristic of Blockchain 358
14.4 Types of Blockchain 366
14.5 Blockchain Platforms 368
14.6 Blockchain Products 370
14.7 Limitations of the Block Chain 373
14.8 Platform Engineering 378
14.9 DevOps in the Blockchain Industry 380
14.10 Results and Discussion 382
14.11 Conclusion 386
14.12 Future Work 387

15 A Multifaceted Approach to Lung Cancer Detection and Segmentation: Platforms, Algorithms, and Emerging Technologies 391
S.S. Uma, S.N. Sindhu Bairavi, R. Saranya, J. Assis Nevatha and Olusola Kunle Akinde

15.1 Introduction 392
15.2 Literature Survey 394
15.3 Proposed System 395
15.4 Result and Discussion 406
15.5 Conclusion 416

16 An AI-Augmented IoT System for Small-Scale Cold Chain Applications 419
Divya James and T.K.S. Lakshmi Priya

16.1 Introduction 420
16.2 Cold Chains 421
16.3 MSME's in Cold Chain 424
16.4 Need for an Architecture 426
16.5 Proposed Architecture 428
16.6 Experimental Evaluations 431
16.7 Application of AI in Cold Chain Systems 441
16.8 Quantitative Analysis of AI for IoT-Enabled Cold Chain Management 444
16.9 Conclusion 450
Bibliography 450

17 A Data-Driven Framework for Crop Price Prediction Using ML, Statistical, and Hybrid Ensemble Models 455
Manimegalai R., Logendar G., Srirengapriya G. and Ayesha S.K.

17.1 Introduction 456
17.2 Literature Survey 457
17.3 Methodologies 460
17.4 Experimental Results 466
17.5 Conclusions and Future Work 474

References 475
Index 477

Über Autor(innen)

K. Suresh Kumar, PhD, is an Assistant Professor at the Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India. He has presented papers in various national and international conferences and journals, written many chapters, and has one copyright and one patent to his credit. His fields of interest include computer networks, data analytics, and natural language processing.

S. Sundaresan, PhD, is an Assistant Professor at the Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India. He has published more than 13 book chapters, 17 articles in various conferences and journals, and has two patents. His areas of research focus on wireless communication and networks.

R. Prithiviraj, PhD, is an Assistant Professor in the Department of Electronics and Communication Engineering at the SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India. He specializes in radiation-hardened design for high-frequency circuits, including PLLs and clock generators.

T. Ananth Kumar, PhD, is an Associate Professor at the Dept. of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamil Nadu, India. He has presented papers various at national and international conferences and journals, published seven books and holds six patents. His fields of interest are networks on chips, computer architecture, and ASIC design.

S. Balamurugan, PhD, is the Director of Research at iRCS, an Indian Technological Research and Consulting Firm. He has published 75 books, 300 papers in international journals and conferences and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision making for leading companies.