{"product_id":"intelligent-scheduling-of-tasks-for-cloud-edge-device-computing-systems-isbn-9781394361625","title":"Intelligent Scheduling of Tasks for Cloud-Edge-Device Computing Systems","description":"\u003cp\u003e\u003cb\u003eComprehensive overview of recent research advancements in scheduling approaches for cloud edge computing systems\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntelligent Scheduling of Tasks for Cloud-Edge-Device Computing Systems\u003c\/i\u003e 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.  \u003c\/p\u003e\u003cp\u003eThe book then summarizes the authors’ research findings in recent years, delving into topics including resource management, latency and real-time requirements, load balancing, \u003c\/p\u003e\u003cp\u003epriority 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. \u003c\/p\u003e\u003cp\u003eAdditional topics include: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eWorkload-aware scheduling of real-time independent tasks, covering how to schedule jobs in a single or multiple servers\u003c\/li\u003e \u003cli\u003eMixed real-time task scheduling in automotive systems with vehicle networks, covering hybrid schedule design, offline task management, and online job assignment\u003c\/li\u003e \u003cli\u003eScheduling with real-time constraint, covering task placement adjustment strategy, start time adjustment, and backwards schedule adjustment\u003c\/li\u003e \u003cli\u003eEnergy-efficient scheduling without real-time constraint, covering energy consumption-optimal task placement plans as well as partition scheduling\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eIntelligent Scheduling of Tasks for Cloud-Edge-Device Computing Systems\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eContents\u003c\/p\u003e \u003cp\u003eForeword\u003c\/p\u003e \u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003eGlossaries\u003c\/p\u003e \u003cp\u003eAcronyms\u003c\/p\u003e \u003cp\u003eAbout the author\u003c\/p\u003e \u003cp\u003eAcknowledgement\u003c\/p\u003e \u003cp\u003e1 Introduction\u003c\/p\u003e \u003cp\u003e1.1 Cloud-Edge-Device Computing Systems\u003c\/p\u003e \u003cp\u003e1.2 Tasks\u003c\/p\u003e \u003cp\u003e1.3 Task Scheduling\u003c\/p\u003e \u003cp\u003e1.4 Outline of the Book\u003c\/p\u003e \u003cp\u003e1.5 Summary\u003c\/p\u003e \u003cp\u003e2 Scheduling Mixed Real-time Tasks in an Automotive System with Vehicular Network\u003c\/p\u003e \u003cp\u003e2.1 Introduction\u003c\/p\u003e \u003cp\u003e2.2 Related Work\u003c\/p\u003e \u003cp\u003e2.3 Models and Problem Formulation\u003c\/p\u003e \u003cp\u003e2.3.1 Software Model\u003c\/p\u003e \u003cp\u003e2.3.2 Hardware Model\u003c\/p\u003e \u003cp\u003e2.4 Hybrid Scheduler Design\u003c\/p\u003e \u003cp\u003e2.5 Schedulability Test\u003c\/p\u003e \u003cp\u003e2.5.1 Utilization-based Schedulability Test\u003c\/p\u003e \u003cp\u003e2.5.2 Demand-Supply Analysis\u003c\/p\u003e \u003cp\u003e2.6 Offline Task Assignment\u003c\/p\u003e \u003cp\u003e2.6.1 Problem Formulation\u003c\/p\u003e \u003cp\u003e2.6.2 Hard Real-Time Task Assignment\u003c\/p\u003e \u003cp\u003e2.6.3 Soft Real-Time Task Assignment\u003c\/p\u003e \u003cp\u003e2.6.4 Complexity Analysis\u003c\/p\u003e \u003cp\u003e2.7 Online Job Assignment\u003c\/p\u003e \u003cp\u003e2.7.1 Online Schedulability Test\u003c\/p\u003e \u003cp\u003e2.7.2 Job Assignment Strategy\u003c\/p\u003e \u003cp\u003e2.7.3 Complexity Analysis\u003c\/p\u003e \u003cp\u003e2.8 Performance Evaluation\u003c\/p\u003e \u003cp\u003e2.8.1 Compared Approaches\u003c\/p\u003e \u003cp\u003e2.8.2 Schedulability Test Results\u003c\/p\u003e \u003cp\u003e2.8.3 Online Job Assignment Tests\u003c\/p\u003e \u003cp\u003e2.9 Summary\u003c\/p\u003e \u003cp\u003e3 Workload-Aware Scheduling of Real-Time Independent Tasks in Cloud\u003c\/p\u003e \u003cp\u003e3.1 Introduction\u003c\/p\u003e \u003cp\u003e3.2 Related Work\u003c\/p\u003e \u003cp\u003e3.3 Related Models\u003c\/p\u003e \u003cp\u003e3.3.1 Virtual CPU Model\u003c\/p\u003e \u003cp\u003e3.3.2 Real-Time Job Model\u003c\/p\u003e \u003cp\u003e3.3.3 Power Model of Virtual Machine\u003c\/p\u003e \u003cp\u003e3.4 Problem Formulation\u003c\/p\u003e \u003cp\u003e3.4.1 Input\u003c\/p\u003e \u003cp\u003e3.4.2 Output\u003c\/p\u003e \u003cp\u003e3.4.3 Constraints\u003c\/p\u003e \u003cp\u003e3.4.4 Objective\u003c\/p\u003e \u003cp\u003e3.5 Scheduling Jobs in a Single Server\u003c\/p\u003e \u003cp\u003e3.5.1 Power Analysis\u003c\/p\u003e \u003cp\u003e3.5.2 Problem Transformation\u003c\/p\u003e \u003cp\u003e3.5.3 Dynamic Programming\u003c\/p\u003e \u003cp\u003e3.6 Scheduling Jobs in Multiple Servers\u003c\/p\u003e \u003cp\u003e3.6.1 Server Energy Efficiency\u003c\/p\u003e \u003cp\u003e3.6.2 Job Placement in Multiple Servers\u003c\/p\u003e \u003cp\u003e3.7 Online Workload-Aware Scheduling\u003c\/p\u003e \u003cp\u003e3.7.1 Job Frequency Profile\u003c\/p\u003e \u003cp\u003e3.7.2 Energy-Efficient Job Accommodation Scheme\u003c\/p\u003e \u003cp\u003e3.8 Performance Evaluation\u003c\/p\u003e \u003cp\u003e3.8.1 Simulation Setup\u003c\/p\u003e \u003cp\u003e3.8.2 Compared Approaches\u003c\/p\u003e \u003cp\u003e3.8.3 Results\u003c\/p\u003e \u003cp\u003e3.9 Summary\u003c\/p\u003e \u003cp\u003e4 Energy-Minimized Scheduling of Real-Time Dependent Tasks in Cloud\u003c\/p\u003e \u003cp\u003e4.1 Introduction\u003c\/p\u003e \u003cp\u003e4.2 Related Work\u003c\/p\u003e \u003cp\u003e4.3 Problem Formulation\u003c\/p\u003e \u003cp\u003e4.3.1 Inputs\u003c\/p\u003e \u003cp\u003e4.3.2 Output\u003c\/p\u003e \u003cp\u003e4.3.3 Objective\u003c\/p\u003e \u003cp\u003e4.3.4 Constraints\u003c\/p\u003e \u003cp\u003e4.4 Energy-Efficient Scheduling Without Real-Time Constraint\u003c\/p\u003e \u003cp\u003e4.4.1 Energy Consumption-Minimized Task Placement Plan\u003c\/p\u003e \u003cp\u003e4.4.2 Partition Scheduling\u003c\/p\u003e \u003cp\u003e4.5 Scheduling with Real-Time Constraint\u003c\/p\u003e \u003cp\u003e4.5.1 Task Placement Adjustment Strategy\u003c\/p\u003e \u003cp\u003e4.5.2 Start Time Adjustment\u003c\/p\u003e \u003cp\u003e4.5.3 Schedule Adjustment in a Backward Way\u003c\/p\u003e \u003cp\u003e4.6 Performance Evaluation\u003c\/p\u003e \u003cp\u003e4.6.1 Simulation Setup\u003c\/p\u003e \u003cp\u003e4.6.2 Compared Approaches\u003c\/p\u003e \u003cp\u003e4.6.3 Results\u003c\/p\u003e \u003cp\u003e4.7 Summary\u003c\/p\u003e \u003cp\u003e5 Workload-Aware Scheduling of Real-Time Dependent Tasks in Vehicular Edge Computing\u003c\/p\u003e \u003cp\u003e5.1 Introduction\u003c\/p\u003e \u003cp\u003e5.2 Related Work\u003c\/p\u003e \u003cp\u003e5.3 Models and Problem Formulation\u003c\/p\u003e \u003cp\u003e5.3.1 Vehicular Computing Model\u003c\/p\u003e \u003cp\u003e5.3.2 Application Model\u003c\/p\u003e \u003cp\u003e5.3.3 Power Model\u003c\/p\u003e \u003cp\u003e5.3.4 Response Time Model\u003c\/p\u003e \u003cp\u003e5.3.5 Problem Formulation\u003c\/p\u003e \u003cp\u003e5.4 Decentralized Auction-Bid Scheduling Scheme\u003c\/p\u003e \u003cp\u003e5.4.1 Auction-Bid Strategy\u003c\/p\u003e \u003cp\u003e5.4.2 Task Prioritization\u003c\/p\u003e \u003cp\u003e5.4.3 Task Assignment and Execution\u003c\/p\u003e \u003cp\u003e5.4.4 Power Management\u003c\/p\u003e \u003cp\u003e5.5 Group Scheduling Scheme\u003c\/p\u003e \u003cp\u003e5.5.1 Task Execution of Multiple Applications\u003c\/p\u003e \u003cp\u003e5.5.2 Application Group and Allocation\u003c\/p\u003e \u003cp\u003e5.6 Evaluation\u003c\/p\u003e \u003cp\u003e5.6.1 Simulation Setup\u003c\/p\u003e \u003cp\u003e5.6.2 Performance Results\u003c\/p\u003e \u003cp\u003e5.7 Summary\u003c\/p\u003e \u003cp\u003e6 Scheduling Multiple-Criticality Dependent Tasks in Vehicular Edge Computing System\u003c\/p\u003e \u003cp\u003e6.1 Introduction\u003c\/p\u003e \u003cp\u003e6.2 Related Work\u003c\/p\u003e \u003cp\u003e6.3 Problem Formulation\u003c\/p\u003e \u003cp\u003e6.3.1 Input\u003c\/p\u003e \u003cp\u003e6.3.2 Output\u003c\/p\u003e \u003cp\u003e6.3.3 Constraints\u003c\/p\u003e \u003cp\u003e6.4 Response Time Analysis\u003c\/p\u003e \u003cp\u003e6.4.1 Task's Response Time in a Virtual Machine\u003c\/p\u003e \u003cp\u003e6.4.2 Application's Response Time\u003c\/p\u003e \u003cp\u003e6.5 Scheduling at 1-Level Mode\u003c\/p\u003e \u003cp\u003e6.5.1 Application Decomposition\u003c\/p\u003e \u003cp\u003e6.5.2 State-Transition Equation\u003c\/p\u003e \u003cp\u003e6.5.3 Dynamic Programming\u003c\/p\u003e \u003cp\u003e6.6 Mixed-Criticality Scheduling\u003c\/p\u003e \u003cp\u003e6.6.1 Mixed-Criticality Schedulability Test\u003c\/p\u003e \u003cp\u003e6.6.2 Online Management by Frequency Prediction\u003c\/p\u003e \u003cp\u003e6.7 Performance Evaluation\u003c\/p\u003e \u003cp\u003e6.7.1 Compared Approaches\u003c\/p\u003e \u003cp\u003e6.7.2 Results\u003c\/p\u003e \u003cp\u003e6.8 Summary\u003c\/p\u003e \u003cp\u003e7 Real-World Applications of Scheduling Technologies\u003c\/p\u003e \u003cp\u003e7.1 Introduction\u003c\/p\u003e \u003cp\u003e7.2 Traffic Management\u003c\/p\u003e \u003cp\u003e7.3 Smart Agriculture with IoT\u003c\/p\u003e \u003cp\u003e7.4 Healthcare Monitoring Systems\u003c\/p\u003e \u003cp\u003e7.5 Concluding Remarks\u003c\/p\u003e \u003cp\u003e8 Summary and Future Research\u003c\/p\u003e \u003cp\u003e8.1 Summary\u003c\/p\u003e \u003cp\u003e8.2 Future Research\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eBiao Hu\u003c\/b\u003e is an Associate Professor with the College of Engineering at China Agricultural University. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMingguo Zhao\u003c\/b\u003e is a Professor with the School of Mechatronics Engineering Harbin Institute of Technology. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eZhengcai Cao\u003c\/b\u003e is a Professor with the College of Information Science and Technology at Beijing University of Chemical Technology. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMengchu Zhou\u003c\/b\u003e is a Professor with the Helen and John C. Hartmann Department of Electrical and Computer Engineering at the New Jersey Institute of Technology.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989441396965,"sku":"NP9781394361625","price":125.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394361625.jpg?v=1761784111","url":"https:\/\/k12savings.com\/products\/intelligent-scheduling-of-tasks-for-cloud-edge-device-computing-systems-isbn-9781394361625","provider":"K12savings","version":"1.0","type":"link"}