國立高雄大學圖資館 |

Energy efficient computation offloading in mobile edge computing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Energy efficient computation offloading in mobile edge computingby Ying Chen ... [et al.].
other author:	Chen, Ying.
Published:	Cham :Springer International Publishing :2022.
Description:	xiv, 156 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Edge computing.
Online resource:	https://doi.org/10.1007/978-3-031-16822-2
ISBN:	9783031168222$q(electronic bk.)

Energy efficient computation offloading in mobile edge computing
Energy efficient computation offloading in mobile edge computing[electronic resource] /by Ying Chen ... [et al.]. - Cham :Springer International Publishing :2022. - xiv, 156 p. :ill., digital ;24 cm. - Wireless networks,2366-1445. - Wireless networks..

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.

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.

ISBN: 9783031168222$q(electronic bk.)

Standard No.: 10.1007/978-3-031-16822-2doiSubjects--Topical Terms:

875964
Edge computing.

LC Class. No.: QA76.583

Dewey Class. No.: 005.25

Energy efficient computation offloading in mobile edge computing
LDR:05704nmm a2200337 a 4500 001 631183
003 DE-He213
005 20221030200947.0
006 m d
007 cr nn 008maaau
008 230411s2022 sz s 0 eng d
020 $a 9783031168222$q(electronic bk.)
020 $a 9783031168215$q(paper)
024 7 $a 10.1007/978-3-031-16822-2 $2 doi
035 $a 978-3-031-16822-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.583
072 7 $a UKN $2 bicssc
072 7 $a COM075000 $2 bisacsh
072 7 $a UKN $2 thema
082 0 4 $a 005.25 $2 23
090 $a QA76.583 $b .E56 2022
245 0 0 $a Energy efficient computation offloading in mobile edge computing $h [electronic resource] / $c by Ying Chen ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xiv, 156 p. : $b ill., digital ; $c 24 cm.
490 1 $a Wireless networks, $x 2366-1445
505 0 $a 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.
520 $a 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.
650 0 $a Edge computing. $3 875964
650 0 $a Edge computing $x Energy consumption. $3 936826
650 0 $a Mobile computing. $3 185560
650 0 $a Mobile computing $x Energy consumption. $3 936827
650 1 4 $a Computer Communication Networks. $3 218087
650 2 4 $a Wireless and Mobile Communication. $3 820685
650 2 4 $a Communications Engineering, Networks. $3 273745
700 1 $a Chen, Ying. $3 933589
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Wireless networks. $3 731203
856 4 0 $u https://doi.org/10.1007/978-3-031-16822-2
950 $a Computer Science (SpringerNature-11645)