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

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