Studies in Autonomic, Data-Driven and Industrial Computing : From Core Concepts to Intelligent Autoscaling for Cloud Applications - Bablu Kumar

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Studies in Autonomic, Data-Driven and Industrial Computing

From Core Concepts to Intelligent Autoscaling for Cloud Applications

By: Bablu Kumar, Anshul Verma, Pradeepika Verma

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Hardcover | 6 February 2026

At a Glance

Hardcover
304 Pages

Dimensions(cm)
23.39 x 15.6 x 1.75

Hardcover

$414.75

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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.

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