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, Beijing, China. Mingguo Zhao is a Professor with the Department of Automation at Tsinghua University, Beijing, China. Zhengcai Cao is a Professor with the School of Mechatronics Engineering at Harbin Institute of Technology, Harbin, China. 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, Newark, NJ, USA.