Modern Kubernetes: From Core Concepts to Intelligent Autoscaling for Cloud Applications (Studies in Autonomic, Data-driven and Industrial Computing)

This book provides an in-depth exploration of Kubernetes, focusing on container orchestration and cluster communication between master and worker nodes. It covers Docker and Swarm for scalability and fault tolerance, along with storage, security, and scaling strategies. The book delves into etcd, the distributed key-value store that maintains Kubernetes cluster state, highlighting its role in consistency through the Raft consensus algorithm. It examines reactive and proactive autoscaling, comparing Horizontal, Vertical, and predictive models leveraging machine learning, statistical methods, fuzzy logic, and deep reinforcement learning. The MAPE (Monitor, Analyze, Plan, Execute) framework is explored for optimizing resource allocation and adapting to workload variations. Additionally, the book discusses Pod deployment, ReplicaSets, and StatefulSets to ensure application reliability and fault tolerance. Security aspects, including RBAC, network policies, and encryption for Kubernetes secrets, are thoroughly covered. To support professional growth, the book includes a section on Kubernetes certification and career paths, featuring review questions, key takeaways, and summaries for easy comprehension. With real-world examples and best practices, this book equips readers to effectively manage Kubernetes environments while balancing performance, scalability, and security.

Mr. Bablu Kumar received an M.C.A. degree in Computer Science and Engineering from Pondicherry University, Kalapet, Puducherry. He qualified for JRF in 2021 and is currently a research scholar under the supervision of Dr. Anshul Verma in the Department of Computer Science at the Institute of Science, Banaras Hindu University, Varanasi (India). His primary research interests include Cloud Computing, Edge Computing with Autoscaling and Resource Allocation, Docker, Kubernetes, Intelligent Systems, Distributed Systems, Deep Learning, and Federated Learning with Large Language Models (LLMs).

Dr. Anshul Verma received M.Tech. and Ph.D. degrees in Computer Science and Engineering from ABV-Indian Institute of Information Technology and Management Gwalior, India. He has done Post-Doctorate from Indian Institute of Technology Kharagpur, India. Currently, he is serving as an Assistant Professor in the Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India. He has also served as a faculty member in the Computer Science and Engineering Department at Motilal Nehru National Institute of Technology (MNNIT) Allahabad and National Institute of Technology (NIT) Jamshedpur, India. His research interests include Cloud Computing, Distributed Systems, Mobile ad-hoc Networks, and Formal Verification. He is serving as an Associate Editor of the Journal of Scientific Research of the Banaras Hindu University.

Dr. Pradeepika Verma received her Ph.D. degree in Computer Science and Engineering from the Indian Institute of Technology (ISM) Dhanbad, India. She has received M.Tech in Computer Science and Engineering from Banasthali University, Rajasthan, India. Currently, she is working as an Assistant Professor at School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India. She has worked as a Faculty Fellow in Technical Innovation Hub at Indian Institute of Technology, Patna, India and as a Post-Doctoral Fellow in the Department of Computer Science and Engineering at Indian Institute of Technology (BHU), Varanasi, India. She has also worked as an Assistant Professor in the Department of Computer Science and Engineering at Pranveer Singh Institute of Technology, Kanpur, India, and as a Faculty Member in the Department of Computer Application at the Institute of Engineering and Technology, Lucknow, India. Her current research interests include Cloud Computing, Distributed Systems, Natural Language Processing, Optimization Approaches, and Mobile ad-hoc Networks.