Energy Efficient Computation Offloading in Mobile Edge Computing

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.-

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,