Scheduling Tasks in Distributed Cloud and Edge Computing Systems with Evolutionary Optimizers

Beschreibung

This book focuses on the challenges and solutions for scheduling tasks in distributed cloud and edge computing systems, with a particular emphasis on predicting workload and resources and optimizing performance and resource utilization through innovative algorithms and methodologies. The book provides an in-depth exploration of theoretical and practical aspects across seven comprehensive parts. The book first introduces the key concepts of cloud computing, edge computing, and their convergence in distributed cloud-edge systems. The authors then lay the groundwork for understanding workload prediction, energy management, and integrating cloud-edge infrastructures with large artificial intelligence (AI) models.  The book then presents a detailed examination of workload and resource prediction techniques. Next, task scheduling is explored with a focus on energy efficiency and performance in unmanned aerial vehicles (UAVs), satellite-terrestrial edge networks, etc. The book also delves into integrating large-scale AI models within cloud-edge systems and introduces innovative practices of new infrastructure in cloud-edge systems. Finally, real-world applications of distributed cloud-edge systems are discussed across various domains. This book provides valuable resources for researchers, engineers, and professionals seeking to advance their knowledge of distributed cloud and edge computing systems and their applications in emerging areas.

This book focuses on the challenges and solutions for scheduling tasks in distributed cloud and edge computing systems, with a particular emphasis on predicting workload and resources and optimizing performance and resource utilization through innovative algorithms and methodologies. The book provides an in-depth exploration of theoretical and practical aspects across seven comprehensive parts. The book first introduces the key concepts of cloud computing, edge computing, and their convergence in distributed cloud-edge systems. The authors then lay the groundwork for understanding workload prediction, energy management, and integrating cloud-edge infrastructures with large artificial intelligence (AI) models.  The book then presents a detailed examination of workload and resource prediction techniques. Next, task scheduling is explored with a focus on energy efficiency and performance in unmanned aerial vehicles (UAVs), satellite-terrestrial edge networks, etc. The book also delves into integrating large-scale AI models within cloud-edge systems and introduces innovative practices of new infrastructure in cloud-edge systems. Finally, real-world applications of distributed cloud-edge systems are discussed across various domains. This book provides valuable resources for researchers, engineers, and professionals seeking to advance their knowledge of distributed cloud and edge computing systems and their applications in emerging areas.

Autor*in

Haitao Yuan

Themen in »Scheduling Tasks in Distributed Cloud and Edge Computing Systems with Evolutionary Optimizers«

Edge Computing Distributed Systems Data Centers Energy Optimization Performance Modeling and Analysis Distributed Computing Deep Learning Data-driven Optimization Meta-heuristic Optimization Algorithms Computational Intelligence Intelligent Decisions

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