Intelligent Scheduling of Tasks for Cloud-Edge-Device Computing Systems

Comprehensive overview of recent research advancements in scheduling approaches for cloud edge computing systems
Intelligent Scheduling of Tasks for Cloud-Edge-Device Computing Systems offers an in-depth collection of advanced task scheduling algorithms designed specifically for diverse cloud-edge-device computing systems. After an introductory overview, a series of intelligent scheduling approaches are presented, each specifically designed for a particular scenario within cloud-edge-device computing systems.
The book then summarizes the authors' research findings in recent years, delving into topics including resource management, latency and real-time requirements, load balancing,
priority constraints, algorithm design, and performance evaluation. The book enables readers to achieve efficient allocation of computing, storage, and network resources to optimize resource utilization. Real-world applications of scheduling technologies in smart cities and traffic management, industrial automation and smart factories, and healthcare monitoring systems are given in a separate chapter.
Additional topics include: - Workload-aware scheduling of real-time independent tasks, covering how to schedule jobs in a single or multiple servers - Mixed real-time task scheduling in automotive systems with vehicle networks, covering hybrid schedule design, offline task management, and online job assignment - Scheduling with real-time constraint, covering task placement adjustment strategy, start time adjustment, and backwards schedule adjustment - Energy-efficient scheduling without real-time constraint, covering energy consumption-optimal task placement plans as well as partition scheduling
Intelligent Scheduling of Tasks for Cloud-Edge-Device Computing Systems is an essential resource for researchers and practitioners in the field of IoT seeking to understand specific challenges and requirements associated with task scheduling in cloud-edge-device computing systems.

Biao Hu is an Associate Professor with the College of Engineering at China Agricultural University.
Mingguo Zhao is a Professor with the School of Mechatronics Engineering Harbin Institute of Technology.
Zhengcai Cao is a Professor with the College of Information Science and Technology at Beijing University of Chemical Technology.
Mengchu Zhou is a Professor with the Helen and John C. Hartmann Department of Electrical and Computer Engineering at the New Jersey Institute of Technology.