Overview

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.

Full Product Details

Author:   Biao Hu (China Agricultural University, China) ,  Mingguo Zhao (Tsinghua University, China) ,  Zhengcai Cao (Beijing University of Chemical Technology, China) ,  MengChu Zhou (University Heights, Newark, NJ, USA)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-IEEE Press
ISBN:  

9781394361625

ISBN 10:   1394361629
Pages:   192
Publication Date:   07 December 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

Contents Foreword Preface Glossaries Acronyms About the author Acknowledgement 1 Introduction 1.1 Cloud-Edge-Device Computing Systems 1.2 Tasks 1.3 Task Scheduling 1.4 Outline of the Book 1.5 Summary 2 Scheduling Mixed Real-time Tasks in an Automotive System with Vehicular Network 2.1 Introduction 2.2 Related Work 2.3 Models and Problem Formulation 2.3.1 Software Model 2.3.2 Hardware Model 2.4 Hybrid Scheduler Design 2.5 Schedulability Test 2.5.1 Utilization-based Schedulability Test 2.5.2 Demand-Supply Analysis 2.6 Offline Task Assignment 2.6.1 Problem Formulation 2.6.2 Hard Real-Time Task Assignment 2.6.3 Soft Real-Time Task Assignment 2.6.4 Complexity Analysis 2.7 Online Job Assignment 2.7.1 Online Schedulability Test 2.7.2 Job Assignment Strategy 2.7.3 Complexity Analysis 2.8 Performance Evaluation 2.8.1 Compared Approaches 2.8.2 Schedulability Test Results 2.8.3 Online Job Assignment Tests 2.9 Summary 3 Workload-Aware Scheduling of Real-Time Independent Tasks in Cloud 3.1 Introduction 3.2 Related Work 3.3 Related Models 3.3.1 Virtual CPU Model 3.3.2 Real-Time Job Model 3.3.3 Power Model of Virtual Machine 3.4 Problem Formulation 3.4.1 Input 3.4.2 Output 3.4.3 Constraints 3.4.4 Objective 3.5 Scheduling Jobs in a Single Server 3.5.1 Power Analysis 3.5.2 Problem Transformation 3.5.3 Dynamic Programming 3.6 Scheduling Jobs in Multiple Servers 3.6.1 Server Energy Efficiency 3.6.2 Job Placement in Multiple Servers 3.7 Online Workload-Aware Scheduling 3.7.1 Job Frequency Profile 3.7.2 Energy-Efficient Job Accommodation Scheme 3.8 Performance Evaluation 3.8.1 Simulation Setup 3.8.2 Compared Approaches 3.8.3 Results 3.9 Summary 4 Energy-Minimized Scheduling of Real-Time Dependent Tasks in Cloud 4.1 Introduction 4.2 Related Work 4.3 Problem Formulation 4.3.1 Inputs 4.3.2 Output 4.3.3 Objective 4.3.4 Constraints 4.4 Energy-Efficient Scheduling Without Real-Time Constraint 4.4.1 Energy Consumption-Minimized Task Placement Plan 4.4.2 Partition Scheduling 4.5 Scheduling with Real-Time Constraint 4.5.1 Task Placement Adjustment Strategy 4.5.2 Start Time Adjustment 4.5.3 Schedule Adjustment in a Backward Way 4.6 Performance Evaluation 4.6.1 Simulation Setup 4.6.2 Compared Approaches 4.6.3 Results 4.7 Summary 5 Workload-Aware Scheduling of Real-Time Dependent Tasks in Vehicular Edge Computing 5.1 Introduction 5.2 Related Work 5.3 Models and Problem Formulation 5.3.1 Vehicular Computing Model 5.3.2 Application Model 5.3.3 Power Model 5.3.4 Response Time Model 5.3.5 Problem Formulation 5.4 Decentralized Auction-Bid Scheduling Scheme 5.4.1 Auction-Bid Strategy 5.4.2 Task Prioritization 5.4.3 Task Assignment and Execution 5.4.4 Power Management 5.5 Group Scheduling Scheme 5.5.1 Task Execution of Multiple Applications 5.5.2 Application Group and Allocation 5.6 Evaluation 5.6.1 Simulation Setup 5.6.2 Performance Results 5.7 Summary 6 Scheduling Multiple-Criticality Dependent Tasks in Vehicular Edge Computing System 6.1 Introduction 6.2 Related Work 6.3 Problem Formulation 6.3.1 Input 6.3.2 Output 6.3.3 Constraints 6.4 Response Time Analysis 6.4.1 Task's Response Time in a Virtual Machine 6.4.2 Application's Response Time 6.5 Scheduling at 1-Level Mode 6.5.1 Application Decomposition 6.5.2 State-Transition Equation 6.5.3 Dynamic Programming 6.6 Mixed-Criticality Scheduling 6.6.1 Mixed-Criticality Schedulability Test 6.6.2 Online Management by Frequency Prediction 6.7 Performance Evaluation 6.7.1 Compared Approaches 6.7.2 Results 6.8 Summary 7 Real-World Applications of Scheduling Technologies 7.1 Introduction 7.2 Traffic Management 7.3 Smart Agriculture with IoT 7.4 Healthcare Monitoring Systems 7.5 Concluding Remarks 8 Summary and Future Research 8.1 Summary 8.2 Future Research Index

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Author Information

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.

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