Ying Chen: Energy Efficient Computation Offloading in Mobile Edge Computing, Kartoniert / Broschiert

Klappentext

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.