Bablu Kumar – författare
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3 produkter
3 produkter
Inbunden, Engelska, 2026
1 837 kr
Skickas inom 5-8 vardagar
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.
E-bok
Engelska, 20262 366 kr
Läs direkt efter köp
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.
Inbunden, Engelska, 2026
621 kr
Kommande
Cloud and edge computing have rapidly transformed the foundation of modern digital systems. They now support a wide range of services that include large scale data processing, real time analytics, artificial intelligence workloads, mobile applications, and Internet of Things deployments. As these environments continue to expand in size, complexity, and diversity, the ability to manage resources efficiently has become a central requirement. Among the various management functions, load balancing plays a vital role in maintaining system performance, reliability, and user experience. It ensures that workloads are distributed fairly across cloud servers, edge nodes, and connected devices so that no single component becomes overloaded. This book titled Intelligent Load Balancing Strategies in Cloud and Edge Computing provides a comprehensive study of the techniques, tools, and intelligent approaches used today to address these challenges. The chapters in this book are designed to move from foundational ideas to advanced methods. Readers begin with the core principles of cloud computing, service models, virtualization, and the characteristics that shape resource management. The book then explores the role and evolution of load balancing, covering classical algorithms, methods, and machine learning based strategies. It also presents the growing importance of edge computing, highlighting the unique issues of mobility, energy constraints, and dynamic workloads at the network edge. A dedicated section offers a detailed taxonomy of algorithms, followed by a comparative study of static, dynamic, hybrid, and nature inspired techniques. Implementation oriented chapters describe simulation tools, programming platforms, scheduling methods, and optimization techniques. Finally, the book integrates these ideas through practical examples, cloud edge coordination models, real world scenarios, and discussions on current research trends. This structured approach makes the book suitable for learners, researchers, and professionals who aim to understand and develop effective load balancing solutions for distributed cloud and edge environments.