000 08086nam a22003855i 4500
999 _c398230
_d398230
001 398230
003 ES-MaUEC
005 20240430115649.0
006 a||||fo|||| 00| 0
007 cr nn 008mamaa
008 230622s2023 sz | o |||| 0|eng d
020 _a9783031339202
024 7 _a10.1007/978-3-031-33920-2
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQA76.585
_b2023 EB
100 1 _aTran-Dang, Hoa
_eautor
_4http://id.loc.gov/vocabulary/relators/aut
_9689616
245 1 0 _aCooperative and Distributed Intelligent Computation in Fog Computing :
_bConcepts, Architectures, and Frameworks
_cby Hoa Tran-Dang, Dong-Seong Kim
250 _a1st ed 2023
264 1 _aCham
_bSpringer Nature Switzerland
_c2023
300 _a1 recurso en línea
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aFog 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.
520 _aThis 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.
988 _aSpringer_Computer_2023
650 7 _2embne
_9666069
_aInformática en la nube
700 1 _9690193
_aKim, Dong Seong
_eautor
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-33920-2
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b02/2024
_dz
_ek
_zSI