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

Inbunden, Engelska, 2026

AvBiao Hu,Mingguo Zhao,Zhengcai Cao,MengChu Zhou

1 759 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

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 serversMixed real-time task scheduling in automotive systems with vehicle networks, covering hybrid schedule design, offline task management, and online job assignmentScheduling with real-time constraint, covering task placement adjustment strategy, start time adjustment, and backwards schedule adjustmentEnergy-efficient scheduling without real-time constraint, covering energy consumption-optimal task placement plans as well as partition schedulingIntelligent 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.

Produktinformation

Utgivningsdatum2026-01-26
Mått152 x 229 x 13 mm
Vikt476 g
FormatInbunden
SpråkEngelska
Antal sidor192
FörlagJohn Wiley & Sons Inc
ISBN9781394361625

Tillhör följande kategorier

Nätverk och kommunikation inom Data och IT

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.

Foreword ixAbout the Authors xiPreface xvAcknowledgments xviiAcronyms xixGlossary xxi1 Introduction 11.1 Cloud-edge-device Computing Systems 11.2 Tasks 21.3 Task Scheduling 31.4 Outline of the Book 41.5 Summary 52 Scheduling Mixed Real-time Tasks in an Automotive System with Vehicular Network 72.1 Introduction 82.2 Related Work 102.3 Models and Problem Formulation 122.3.1 Software Model 122.3.2 Hardware Model 122.4 Hybrid Scheduler Design 132.5 Schedulability Test 152.5.1 Utilization-based Schedulability Test 152.5.2 Demand-supply Analysis 152.6 Offline Task Assignment 172.6.1 Problem Formulation 172.6.2 Hard Real-time Task Assignment 202.6.3 Soft Real-time Task Assignment 222.6.4 Complexity Analysis 242.7 Online Job Assignment 242.7.1 Online Schedulability Test 242.7.2 Job Assignment Strategy 272.7.3 Complexity Analysis 282.8 Performance Evaluation 282.8.1 Compared Approaches 292.8.2 Schedulability Test Results 302.8.3 Online Job Assignment Tests 332.9 Summary 353 Workload-aware Scheduling of Real-time Independent Tasks in Cloud 373.1 Introduction 373.2 Related Work 413.3 Related Models 433.3.1 Virtual CPU Model 433.3.2 Real-time Job Model 433.3.3 Power Model of VM 443.4 Problem Formulation 443.4.1 Input 453.4.2 Output 453.4.3 Constraints 453.4.4 Objective 463.5 Scheduling Jobs in a Single Server 473.5.1 Power Analysis 473.5.2 Problem Transformation 473.5.3 Dynamic Programming 483.6 Scheduling Jobs in Multiple Servers 513.6.1 Server Energy Efficiency 513.6.2 Job Placement in Multiple Servers 523.7 Online Workload-aware Scheduling 533.7.1 Job Frequency Profile 543.7.2 Energy-efficient Job Accommodation Scheme 543.8 Performance Evaluation 583.8.1 Simulation Setup 583.8.2 Compared Approaches 583.8.3 Results 593.9 Summary 644 Energy-minimized Scheduling of Real-time Dependent Tasks in Cloud 654.1 Introduction 654.2 Related Work 684.3 Problem Formulation 694.3.1 Input 694.3.2 Output 704.3.3 Objective 714.3.4 Constraints 714.4 Energy-efficient Scheduling Without Real-time Constraint 744.4.1 Energy Consumption-minimized Task Placement Plan 744.4.2 Partition Scheduling 754.5 Scheduling with Real-time Constraint 784.5.1 Task Placement Adjustment Strategy 794.5.2 Start Time Adjustment 824.5.3 Schedule Adjustment in a Backward Way 844.6 Performance Evaluation 854.6.1 Simulation Setup 864.6.2 Compared Approaches 874.6.3 Results 884.7 Summary 935 Workload-aware Scheduling of Real-time Dependent Tasks in Vehicular Edge Computing 955.1 Introduction 955.2 Related Work 975.3 Models and Problem Formulation 995.3.1 Vehicular Computing Model 995.3.2 Application Model 1005.3.3 Power Model 1015.3.4 Response Time Model 1025.3.5 Problem Formulation 1025.4 Decentralized Auction-bid Scheduling Scheme 1035.4.1 Auction-bid Strategy 1035.4.2 Task Prioritization 1045.4.3 Task Assignment and Execution 1045.4.4 Power Management 1065.5 GS Scheme 1075.5.1 Task Execution of Multiple Applications 1075.5.2 Application Group and Allocation 1085.6 Evaluation 1105.6.1 Simulation Setup 1105.6.2 Performance Results 1115.7 Summary 1176 Scheduling Multiple-criticality Dependent Tasks in Vehicular Edge Computing System 1196.1 Introduction 1196.2 Related Work 1226.3 Problem Formulation 1236.3.1 Input 1236.3.2 Output 1256.3.3 Constraints 1256.4 Response Time Analysis 1266.4.1 Task’s Response Time in a VM 1276.4.2 Application’s Response Time 1276.5 Scheduling at Level-1 Mode 1286.5.1 Application Decomposition 1286.5.2 State-transition Equation 1296.5.3 Dynamic Programming 1316.6 Mixed-criticality Scheduling 1326.6.1 Mixed-criticality Schedulability Test 1326.6.2 Online Management by Frequency Prediction 1336.7 Performance Evaluation 1346.7.1 Compared Approaches 1346.7.2 Results 1356.8 Summary 1397 Real-world Applications of Scheduling Technologies 1417.1 Introduction 1417.2 Traffic Management 1417.3 Smart Agriculture with Internet of Things 1437.4 Healthcare Monitoring Systems 1447.5 Summary 1458 Summary and Future Research 1478.1 Summary 1478.2 Future Research 147References 149Index 165