Energy Efficient Computation Offloading In Mobile Edge Computing de Chen, Ying Format Relié

Energy Efficient Computation Offloading in Mobile Edge Computing - Chen, Ying

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Présentation Energy Efficient Computation Offloading In Mobile Edge Computing de Chen, Ying Format Relié
- Livre Technologie

Livre Technologie - Chen, Ying - 01/10/2022 - Relié - Langue : Anglais

Auteur(s) : Chen, Ying - Shen, Sherman - Wu, Yuan - Zhang, Ning

Editeur : Springer International Publishing Ag

Langue : Anglais

Parution : 01/10/2022

Format : Moyen, de 350g à 1kg

Nombre de pages : 172

Expédition : 430

Dimensions : 24.1 x 16.0 x 1.6

ISBN : 3031168216

Résumé :
Introduction.- 1.1 Background.- 1.1.1 Mobile Cloud Computing.- 1.1.2 Mobile Edge Computing.- 1.1.3 Computation Offloading.- 1.2 Challenges.- 1.3 Contributions.- 1.4 Book Outline.- References.- 2 Dynamic Computation Offloading for Energy Efficiency in Mobile.- Edge Computing.- 2.1 System Model and Problem Statement.- 2.1.1 Network Model.- 2.1.2 Task Offloading Model.- 2.1.3 Task Queuing Model.- 2.1.4 Energy Consumption Model.- 2.1.5 Problem Statement.- 2.2 EEDCO: Energy Efficient Dynamic Computing Offloading for.- Mobile Edge Computing.- 2.2.1 Joint Optimization of Energy and Queue.- 2.2.2 Dynamic Computation Offloading for Mobile Edge.- Computing.- 2.2.3 Trade-off Between Queue Backlog and Energy Efficiency.- 2.2.4 Convergence and Complexity Analysis.- 2.3 Performance Evaluation.- 2.3.1 Impacts of Parameters.- 2.3.2 Performance Comparison with EA and QW Schemes.- 2.4 Literature Review.- 2.5 Summary.- References.- ix.- x Contents.- 3 Energy Efficient Offloading and Frequency Scaling forInternet of.- Things Devices.- 3.1 System Model and Problem Formulation.- 3.1.1 Network Model.- 3.1.2 Task Model.- 3.1.3 Queuing Model.- 3.1.4 Energy Consumption Model.- 3.1.5 Problem Formulation.- 3.2 COFSEE:Computation Offloading and Frequency Scaling for.- Energy Efficiency of Internet of Things Devices.- 3.2.1 Problem Transformation.- 3.2.2 Optimal Frequency Scaling.- 3.2.3 Local Computation Allocation.- 3.2.4 MEC Computation Allocation.- 3.2.5 Theoretical Analysis.- 3.3 Performance Evaluation.- 3.3.1 Impacts of System Parameters.- 3.3.2 Performance Comparison with RLE,RME and TS Schemes.- 3.4 Literature Review.- 3.5 Summary.- References.- 4 Deep Reinforcement Learning for Delay-aware and Energy-Efficient.- Computation Offloading.- 4.1 System Model and Problem formulation.- 4.1.1 System Mode.- 4.1.2 Problem Formulation.- 4.2 Proposed DRL Method.- 4.2.1 Data prepossessing.- 4.2.2 DRL Model.- 4.2.3 Training.- 4.3 Performance Evaluation.- 4.4 Literature Review.- 4.5 Summary.- References.- 5 Energy-Efficient Multi-task Multi-access Computation Offloading.- via NOMA.- 5.1 System Model and Problem Formulation.- 5.1.1 Motivation.- 5.1.2 System Model.- 5.1.3 Problem Formulation.- 5.2 LEEMMO: Layered Energy-efficient Multi-task Multi-access.- Algorithm.- 5.2.1 Layered Decomposition of Joint Optimization Problem.- Contents xi.- 5.2.2 Proposed Subroutine for Solving Problem (TEM-E-Sub).- 5.2.3 A Layered Algorithm for Solving Problem (TEM-E-Top).- 5.2.4 DRL-based Online Algorithm.- 5.3 Performance Evaluation.- 5.3.1 Impacts of Parameters.- 5.3.2 Performance Comparison with FDMA based Offloading.- Schemes.- 5.4 Literature Review.- 5.5 Summary.- Reference.- 6 Conclusion.- 6.1 Concluding Remarks.- 6.2 Future Directions.- References. ...

Biographie:

?Ying Chen received the BEng degree from the School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China, in 2012, and the PhD degree in computer science and technology from Tsinghua University, Beijing, in 2017. She is currently an associate professor with Computer School, Beijing Information Science and Technology University, Beijing. She was a joint PhD student in University of Waterloo from 2015 to 2016. She is the recipient of the Best Paper Award at IEEE SmartIoT 2019, 2016 Google Ph. D Fellowship Award and 2014 Google Anita Borg Award, respectively. She is the TPC member of IEEE HPCC, and PC member of IEEE Cloud, CollaborateCom, IEEE CPSCom, CSS, etc. She is also the reviewer of several journals such as IEEE Transactions on Dependable and Secure Computing, IEEE Internet of Things Journal, IEEE Transactions on Computers, IEEE Transactions on Cloud Computing, IEEE Transactions on Services Computing, etc. Her current research interests include mobile edge computing, wireless networks and communications, machine learning, etc.

Ning Zhang received the B.Sc. degree from Beijing Jiaotong University, Beijing, China, the M.Sc. degree from Beijing University of Posts and Telecommunications, Beijing, China, and the Ph.D. degree from the University of Waterloo, Waterloo, ON, Canada, in 2007, 2010, and 2015, respectively. After that, he was a postdoc research fellow at University of Waterloo and University of Toronto, Canada, respectively. He is now an Associate Professor in the Department of Electrical and Computer Engineering at University of Windsor, Canada. His research interests include connected vehicles, mobile edge computing, wireless networking, and machine learning. He has published over 180 refereed papers in international journals and conferences. He is a Highly Cited Researcher and on the World's Top 2% Scientists list by Stanford University. He serves as an Associate Editor of IEEE Internet of Things Journal, IEEE Transactions on Cognitive Communications and Networking, and IEEE Systems Journal...

Sommaire:

This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices' delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce anend-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions.

Researchers working in mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book.

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Energy Efficient Computation Offloading in Mobile Edge Computing - Chen, Ying

Avis sur Energy Efficient Computation Offloading In Mobile Edge Computing de Chen, Ying Format Relié - Livre Technologie

Présentation Energy Efficient Computation Offloading In Mobile Edge Computing de Chen, Ying Format Relié - Livre Technologie

Présentation Energy Efficient Computation Offloading In Mobile Edge Computing de Chen, Ying Format Relié
- Livre Technologie