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Cooperative and Distributed Intelligent Computation in Fog Computing Concepts, Architectures, and Frameworks /

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 incl...

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Bibliografiske detaljer
Main Authors: Tran-Dang, Hoa (Author), Kim, Dong-Seong (Author)
Institution som forfatter: SpringerLink (Online service)
Format: Electronisk eBog
Sprog:engelsk
Udgivet: Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
Udgivelse:1st ed. 2023.
Fag:
Cloud Computing.
Mobile computing.
Computer Networks.
Mobile Computing.
Online adgang:https://doi.org/10.1007/978-3-031-33920-2
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Indholdsfortegnelse:
  • Fog Computing: Concepts & Recent Advances
  • 1.1 Introduction
  • 1.2 Fog Computing Architectures
  • 1.2.1 Hierarchical Architecture Model
  • 1.2.2 Layered Architecture Model
  • 1.3 Computation Offloading in Fog Computing Architectures
  • 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
  • 2 Applications of Fog Computing
  • 2.1 Introduction
  • 2.2 Typical Applications of Fog Computing
  • 2.2.1 Healthcare
  • 2.2.2 Smart Cities
  • 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
  • 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 Model
  • 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
  • 4 Fog Resource Aware Framework for Adaptive Task Offloading in Fog-based 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
  • 4.4.2 Mathematical Formulation of FRATO
  • 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
  • 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
  • 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 Computation 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
  • 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
  • 6.4 Matching Algorithms
  • 6.5 Conclusions
  • 7 Matching Theory for Distributed Computation Offloading in Fog Computing Systems
  • 7.1 Introduction
  • 7.2 System and Offloading Problem Description
  • 7.2.1 System Model
  • 7.2.2 Computation Tasks
  • 7.2.3 Computation Offloading Models
  • 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
  • 7.3.2 Many-to-One (MTO) Matching7
  • 7.3.3 Many-to-Many (MTM) Matching
  • 7.4 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
  • 8 Distributed Computation Offloading Frameworks for Fog 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
  • 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 Algorithms
  • 8.5.4 Optimal Task Offloading and Communication Scheduling 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
  • 9 Reinforcement Learning-based Resource Allocations 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
  • 9.3.2 Taxonomy of RL Algorithms
  • 9.4 RL based Algorithms for Resource Allocation in FC Systems
  • 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
  • 9.5.2 Fog Computing Environment related Challenges
  • 9.5.3 Computation Task related Challenges
  • 9.6 Conclusions and Discussions
  • 10 Bandit Learning and Matching based Distributed Task Offloading in Fog Networks
  • 10.1 Introduction
  • 10.2 Bacground and Related Works
  • 10.2.1 One-to-One Matching-based Task Offloading
  • 10.2.2 Bandit Learning-based Computation Offloading
  • 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 and Discussions. .

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