Energy efficient computation offloading in mobile edge computing / Ying Chen, Ning Zhang, Yuan Wu, Sherman Shen.
Chen, Ying, author.; Zhang, Ning (Computer scientist), author.; Wu, Yuan, author.; Shen, X. (Xuemin), 1958- author.
2022
QA76.583
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Title Energy efficient computation offloading in mobile edge computing / Ying Chen, Ning Zhang, Yuan Wu, Sherman Shen.
Author Chen, Ying, author.
ISBN 9783031168222 (electronic bk.)
3031168224 (electronic bk.)
9783031168215
3031168216
DOI https://doi.org/10.1007/978-3-031-16822-2
Published Cham : Springer, 2022.
Language English
Description 1 online resource : illustrations (black and white).
Item Number 10.1007/978-3-031-16822-2 doi
Call Number QA76.583
Dewey Decimal Classification 005.25
Summary 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 an end-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.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Zhang, Ning (Computer scientist), author.
Wu, Yuan, author.
Shen, X. (Xuemin), 1958- author.
Series Wireless networks (Springer (Firm))
Available in Other Form Energy efficient computation offloading in mobile edge computing.
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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 for Internet 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.

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