Intelligent Scheduling of Tasks for Cloud Edge Device Computing Systems

Bol

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. 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.

Amazon

Pagina's: 192, Editie: Eerste editie, Hardcover, Wiley-IEEE Press