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Description

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.