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Table of contents
1 Introduction
1.1 Context and Motivations
1.1.1 Muti-user computation offloading with edge caching consideration in MEC
1.1.2 Socially-aware MEC
1.1.3 Learning-based computation offloading strategies in MEC
1.2 MEC architecture
1.3 Key issues in 5G MEC
1.4 Problem statement
1.5 Thesis contributions
1.5.1 Proposed an optimal offloading with caching-enhancement scheme (OOCS) for femto-cloud scenario and mobile edge computing scenario, respectively
1.5.2 Proposed a device-to-device (D2D) multicast-based computation offload- ing strategy
1.5.3 Proposed a socially-aware hybrid (D2D/MEC) computation offloading (SAHCO) strategy
1.5.4 Proposed a deep learning based low complexity computation offloading strategy
1.6 Thesis outline
2 Background and Literature Review
2.1 Introduction
2.2 Computation offloading
2.2.1 Coarse-grained/full offloading
2.2.2 Fine-grained/partial offloading
2.3 Mobile application model
2.3.1 Call graph
2.3.2 Mobile augment reality applications
2.3.3 Mobile crowd sensing applications
2.4 Edge caching in MEC
2.4.1 Service caching
2.4.2 Data caching
2.5 Summary and discussion
2.6 Conclusion
3 Computation Offloading with Caching-Enhancement for Mobile Edge Computing
3.1 Introduction
3.2 System Model
3.2.1 Network Model
3.2.2 Application Model
3.2.3 Caching Model
3.2.4 Execution Model
3.2.4.1 Local Execution
3.2.4.2 Low-end Execution
3.2.4.3 High-end Execution
3.3 Proposed collaborative call graph and problem formulation
3.3.1 Collaborative call graph with caching enhancement
3.3.2 Problem formulation for Low-end MEC deployment
3.3.2.1 Single-user Scenario
3.3.2.2 Multi-user scenario
3.3.3 Problem formulation for High-end MEC deployment
3.3.3.1 Single-user Scenario
3.3.3.2 Multi-user scenario
3.4 Proposal: OOCS scheme
3.4.1 Game Formulation
3.4.2 Algorithm Description
3.4.3 Complexity analysis
3.5 Performance evaluation
3.5.1 Performance evaluation of OOCS in single-user scenario
3.5.1.1 Low-end deployment scenario
3.5.1.2 High-end/Low-end performance comparison
3.5.2 Performance evaluation of OOCS in multi-user scenario
3.6 Conclusion
4 D2D-Multicast Based Computation Offloading Frameworks for Mobile Edge Com- puting
4.1 Introduction
4.2 A multicast based D2D computation offloading framework
4.2.1 Information collection through LTE uplink channel
4.2.2 Computation result feedback through D2D multicast channel
4.3 Problem formulation
4.4 Proposal: MWBM-CG scheme
4.5 Performance evaluation
4.5.1 Network parameters
4.5.2 Application parameters
4.5.3 Simulation results
4.6 Conclusion
5 A Socially-Aware Hybrid Computation Offloading Framework for Mobile Edge Computing
5.1 Introduction
5.2 System Model
5.3 A social-aware hybrid computation offloading framework
5.3.1 Description of the proposed offloading framework
5.3.2 D2D Link Model
5.3.3 Social Relationship Model
5.4 Problem formulation
5.4.1 State Space and Action Space
5.4.2 Immediate Cost
5.4.3 Optimal Problem Formulation
5.5 Proposal: TA-MCTS scheme
5.5.1 Selection
5.5.2 Expansion
5.5.3 Simulation
5.5.4 Backpropagation
5.6 Performance Evaluation
5.7 Conclusion
6 Computation Offloading for Multi-Core Devices in 5G Mobile Edge Computing: A Deep Learning Approach
6.1 Introduction
6.2 Decision making procedure and problem formulation
6.2.1 Decision making procedure
6.2.2 State space and action space
6.2.3 Immediate cost
6.2.4 Optimal problem formulation
6.3 Algorithm design
6.3.1 Initial phase
6.3.2 Training phase
6.3.3 Testing phase
6.4 Performance evaluation
6.4.1 Network parameters
6.4.2 Simulation results
6.5 Conclusion and future work
7 Conclusions
7.1 Summary of contributions
7.2 Future work
7.3 Publications
References
