Foundations and Intelligent Strategies for Load Balancing in Cloud and Edge Computing

Foundations and Intelligent Strategies for Load Balancing in Cloud and Edge Computing

von Anshul Verma

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.

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.

Highlights

• Enables systematic understanding and comparison of load balancing strategies Supports practical learning through contemporary simulation and implementation technologies Enhances adaptability, scalability, and relevance for next-generation cloud and edge systems

Über den Autor

Dr. Anshul Verma received his MTech and PhD degrees in Computer Science and Engineering from ABV -Indian Institute of Information Technology and Management (IIITM), Gwalior, India. He pursued his postdoctoral research at the Indian Institute of Technology (IIT) Kharagpur, India. He is currently an Assistant Professor in the Department of Computer Science, Banaras Hindu University, Varanasi, India, and is presently on study leave, serving as a Visiting Researcher at the School of Computing and Information Systems, University of Melbourne, Australia, with over 10 years of academic and research experience. Prior to joining BHU, he was associated with the Department of Computer Science and Engineering at Motilal Nehru National Institute of Technology (MNNIT) Allahabad and the National Institute of Technology (NIT) Jamshedpur as a faculty member. His research interests span Cloud Computing, Distributed Systems, Mobile Ad-Hoc Networks, and Formal Verification. He has successfully organized five editions of the International Conference on Advanced Network Technologies and Intelligent Computing (ANTIC) as General Chair and Convener since 2021.He has published extensively in renowned journals, books, and conferences.    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. Pradeepika Verma received her PhD degree in Computer Science and Engineering from the Indian Institute of Technology (ISM) Dhanbad, India. She has received MTech in Computer Science and Engineering from Banas thali University, Rajasthan, India. She is currently serving as an Assistant Professor in the School of Computer Science Engineering and Technology at 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 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.