This informative text/reference presents a detailed review of the state of the art in fog computing paradigm. In particular, the book examines a broad range of important cooperative and distributed computation algorithms, along with their design objectives and technical challenges.
The coverage includes the conceptual fundamental of fog computing, its practical applications, cooperative and distributed computation algorithms using optimization, swarm intelligence, matching theory, and reinforcement learning methods. Discussions are also provided on remaining challenges and open research issues for designing and developing the efficient distributed computation solutions in the next-generation of fog-enabled IoT systems.
Author(s): Hoa Tran-Dang, Dong-Seong Kim
Publisher: Springer
Year: 2023
Language: English
Pages: 210
City: Cham
Preface
Acknowledgments
Contents
Abbreviations
Chapter 1: Fog Computing: Fundamental Concepts and Recent Advances in Architectures and Technologies
1.1 Introduction
1.2 Fog Computing Architectures
1.2.1 Hierarchical Architecture Model
Terminal Layer
Fog Layer
Cloud Layer
1.2.2 Layered Architecture Model
Physical and Virtualization Layer
Monitoring Layer
Processing Layer
Temporary Storage Layer
Security Layer
Transport Layer
1.3 Computation Offloading in Fog Computing-Based Systems
1.4 Key Technologies for Future Fog Computing Architectures
1.4.1 Communication and Networking Technologies
1.4.2 Virtualization Technologies
1.4.3 Storage Technologies
1.4.4 Privacy and Data Security Technologies
1.5 Conclusions
References
Chapter 2: Applications of Fog Computing
2.1 Introduction
2.2 Typical Applications of Fog Computing
2.2.1 Healthcare
2.2.2 Smart Cities
Surveillance
Congestion Control
Smart Transportation
Energy Consumption
Big Data Analytics
2.2.3 Smart Grid
2.2.4 Industrial Robotics and Automation in Smart Factories
2.2.5 Agriculture
2.2.6 Logistics and Supply Chains
2.3 Summary and Conclusions
References
Chapter 3: Cooperation for Distributed Task Offloading in Fog Computing Networks
3.1 Introduction
3.2 System Model
3.2.1 Fog Computing Networks
3.2.2 Computation Tasks
3.2.3 Computation Offloading Modes
Full Offloading
Partial Offloading
Hybrid Offloading
3.3 Cooperation-Based Task Offloading Models
3.4 Open Research Issues
3.4.1 Data Fragmentation
3.4.2 Distribution of Fog Networks
3.4.3 Advances of Distributed Algorithms
3.4.4 Comprehensive Performance Analysis
3.5 Conclusions
References
Chapter 4: Fog Resource Aware Framework for Task Offloading in IoT Systems
4.1 Introduction
4.2 Related Works
4.3 System Model and Problem Formulation
4.3.1 System Model
4.3.2 Problem Formulation
4.4 FRATO Fog Resource Aware Task Offloading Framework
4.4.1 Offloading Strategies for Minimizing Service Provisioning Delay
Optimal Full Task Offloading in Fog (OFTOF)
Optimal Full Task Offloading in Cloud (OFTOC)
Optimal Collaborative Task Offloading in Fog (OCTOF)
4.4.2 Mathematical Formulation of FRATO
Objective Function
Constraints
4.4.3 Solution Deployment Analysis
4.5 Distributed Resource Allocation in Fog
4.5.1 Task Priority-Based Resource Allocation
4.5.2 Maximal Resource Utilization-Based Allocation
4.6 Simulation and Performance Evaluation
4.6.1 Simulation Environment Setup
Characteristics of IoT Services
System Configuration
Communication Delay and Channel Capacity
4.6.2 Comparative Approaches
4.6.3 Evaluation and Analysis
4.6.4 Further Analysis of Computation Time and Complexity
4.7 Conclusions
4.8 Future Works
4.8.1 Data Fragmentation
4.8.2 Distribution of Fog Networks
4.8.3 Advance of Optimization Algorithms
4.8.4 Comprehensive Performance Analysis
References
Chapter 5: Dynamic Collaborative Task Offloading in Fog Computing Systems
5.1 Introduction
5.2 Related Works
5.3 System Model and Problem Formulation
5.3.1 System Model
5.3.2 Computational Task Model
5.3.3 Problem Formulation
5.4 Optimization Problem for Minimization of Task Execution Delay
5.5 Simulation and Performance Evaluation
5.5.1 Simulation Environment Setup
5.5.2 Evaluation and Analysis
5.6 Conclusions and Future Works
References
Chapter 6: Fundamentals of Matching Theory
6.1 Introduction
6.2 Basic Concepts and Terminologies
6.3 Classification
6.3.1 One-to-One (OTO) Matching
6.3.2 Many-to-One (MTO) Matching
6.3.3 Many-to-Many (MTM) Matching
6.3.4 Variants of Matching Models
Matching with Externaties
Matching with Transfer
Matching with Incentive
Matching with Groups
Matching with Variants of PLs
6.4 Matching Algorithms
6.5 Conclusions
References
Chapter 7: Matching Theory for Distributed Computation in IoT-Fog-Cloud Systems
7.1 Introduction
7.2 System and Offloading Problem Description
7.2.1 System Model
7.2.2 Computational Tasks
7.2.3 Computational Offloading Model
7.2.4 Optimization Problems of Computational Offloading
7.3 Proposed Matching-Based Models for Distributed Computation
7.3.1 One-to-One (OTO) Matching-Based Models
7.3.2 Many-to-One (MTO) Matching-Based Models
7.3.3 Many-to-Many (MTM) Matching-Based Models
7.4 Remaining Challenges and Open Research Issues
7.4.1 Matching with Dynamics
7.4.2 Matching with Groups
7.4.3 Matching with Externality
7.4.4 Security and Privacy of Data and End Users
7.4.5 New Offloading Application Scenarios
7.4.6 Application of AI and ML-Based Techniques
7.5 Conclusions
References
Chapter 8: Distributed Computation Offloading Framework for Fog Computing Networks
8.1 Introduction
8.2 Preliminary and Related Works
8.2.1 Preliminary of Many-to-One (M2O) Matching Model
8.2.2 Related Works
Scheduling for Parallel Offloading and Communication
M2O Models for Computation Offloading
8.3 System Model
8.3.1 Fog Computing Networks
8.3.2 Computation Offloading Model
8.4 Problem Formulation
8.5 Description of DISCO Framework
8.5.1 Overview
8.5.2 PL Construction
8.5.3 Matching Algorithm
8.5.4 Optimal Task Offloading and Communication Scheduling (OTOS) Algorithm
8.5.5 Stability Analysis
8.6 Simulations and Performance Evaluation
8.6.1 Simulation Environment Setup
8.6.2 Evaluation and Analysis
8.7 Conclusions
References
Chapter 9: Reinforcement Learning-Based Resource Allocation in Fog Networks
9.1 Introduction
9.2 Fog Computing Environment
9.2.1 System Model
9.2.2 Resource Allocation Problems in Fog Computing Systems
9.3 Reinforcement Learning
9.3.1 Basic Concepts
State Space
Action Space
Reward and Returns
Policy
State Value and State-Action Value Function
9.3.2 Taxonomy of RL Algorithms
9.4 RL-Based Algorithms for Resource Allocation in FCS
9.4.1 Resource Sharing and Management
9.4.2 Task Scheduling
9.4.3 Task Offloading and Redistribution
9.5 Challenges and Open Issues of RL-Based Resource Allocations
9.5.1 RL-Related Challenges
Nature of RL-Based Algorithms
Load Balancing in RL-Enabled Fog Computing
Task Scheduling in RL-Enabled Fog Computing
Energy-Efficiency Tradeoff in RL-Enabled Fog Computing
Real-Time Responsiveness in RL-Enabled Fog
Advance of Optimization Algorithms
9.5.2 Fog Computing Environment-Related Challenges
RL-Based Resource Allocation in F-RAN
RL-Based Power Consumption Optimization for F-RAN
RL for Ultra-dense Fog Network
Reliability of Fog Networks
Security and Trust in Fog Network
9.5.3 Computing Task-Related Challenges
Big Data Analytics
Data Fragmentation for Parallel Computing Exploitation
9.6 Conclusions
References
Chapter 10: Bandit Learning and Matching-Based Distributed Task Offloading in Fog Networks
10.1 Introduction
10.2 Background and Related Works
10.2.1 One-to-One Matching-Based Computation Offloading Algorithms
10.2.2 Bandit Learning-Based Computation Offloading Algorithms
10.3 System Model
10.3.1 Fog Computing Networks
10.3.2 Computation Offloading Model
10.4 Design of BLM-DTO Algorithm
10.4.1 OTO Matching Model for Computation Offloading
10.4.2 Multi-player Multi-armed Bandit with TS
10.5 Simulation Results and Evaluation Analysis
10.5.1 Simulation Environment Configuration
10.5.2 Comparative Evaluation and Analysis
10.6 Conclusions
References
