5G Edge Computing : Technologies, Applications and Future Visions

• Autore: Ma Xiao
• Pubblicazione: Singapore : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (209 pages)
• Disciplina: 005.758
• Altri autori: XuMengwei LiQing LiYuanzhe ZhouAo WangShangguang
• Nota di contenuto: Intro -- Preface -- Contents -- 1 Background -- 1.1 5G Network and Beyond -- 1.1.1 5G Service Requirements and Network Characteritics -- 1.1.2 6G Network Visions -- 1.1.2.1 Multiple Types of Mobile Communication -- 1.1.2.2 Reliable Low Latency with Mobile Broadband -- 1.1.2.3 Communication Integrated with Artificial Intelligence -- 1.2 Edge Computing -- 1.2.1 Concepts of Edge Computing -- 1.2.2 Edge Computing Standard Progress -- 1.2.3 Advancements of Public Edge Platforms -- 1.3 Key Technologies for Edge Computing in 5G and Beyond -- 1.3.1 Multi-edge Computation Offloading -- 1.3.2 Dynamic Workload Scheduling -- 1.3.3 Edge Caching -- 1.3.4 Edge Resource Provisioning -- 1.4 5G-based Edge Computing System and Edge Computing for 5G Beyond -- 1.4.1 5G-based Edge Computing System -- 1.4.2 Satellite Edge Computing -- 1.4.3 Space-Ground Integrated Computing Architecture in 5G Beyond -- References -- 2 Recent Advancements of Public Edge Platforms -- 2.1 Introduction -- 2.2 The NEP Edge Platform -- 2.3 Network Performance of NEP in Comparison to Public Clouds -- 2.3.1 End-to-End Network Latency -- 2.3.2 End-to-End Network Throughput -- 2.4 Application Performance of NEP in Comparison to Public Clouds -- 2.4.1 Cloud Gaming -- 2.4.2 Live Streaming -- 2.5 Workloads of NEP -- 2.5.1 Applications and VM Subscription -- 2.5.2 Overall Resource Usage -- 2.5.3 Resource Load Balance -- 2.6 Implications -- 2.7 Summary -- References -- 3 Edge Workload Prediction Based on Deep Learning -- 3.1 Introduction -- 3.2 Cloud-Edge Collaborated Edge Workload Prediction Framework -- 3.2.1 Framework Overview -- 3.2.2 Global Stage -- 3.2.3 Local Stage -- 3.3 Experimental Results -- 3.3.1 Experiment Settings -- 3.3.2 Experimental Results -- 3.3.2.1 Overall Performance -- 3.3.2.2 Time Overhead and Communication Cost -- 3.4 Summary -- References. ; 4 Edge Computing Based Computation Offloading -- 4.1 Introduction -- 4.2 Computation Offloading with Deterministic-QoS Guarantee -- 4.2.1 System Model -- 4.2.2 Problem Formulation -- 4.2.3 Algorithm Design and Complexity Analysis -- 4.3 Computation Offloading with Statistic-QoS Guarantee -- 4.3.1 Problem Formulation and Statistical QoS Guarantee Transition -- 4.3.2 Algorithm Design and Performance Analysis -- 4.4 Simulations Results -- 4.5 Summary -- References -- 5 Dynamic Workload Scheduling in Edge Computing -- 5.1 Introduction -- 5.2 Overview of ETSI Cloud-Edge-Device Architecture -- 5.3 Edge-Device Workload Scheduling (Mobile Computing Access Control) -- 5.3.1 Queuing Based Analytical Model -- 5.3.2 Problem Formulation -- 5.3.3 Convex Analysis -- 5.4 Cloud-Edge Workload Scheduling -- 5.4.1 System Model -- 5.4.2 Dynamic Problem Transformation and Performance Analysis -- 5.4.3 Computation Complex Analysis -- 5.5 Water-Filling based Centralized Workload Scheduling -- 5.6 Performance Analysis -- 5.7 Summary -- References -- 6 Edge Service Caching -- 6.1 Introduction -- 6.2 Static Caching -- 6.2.1 Service Placement -- 6.2.2 Jointly Service Placement and Workload Scheduling -- 6.2.2.1 Problem Formulation -- 6.2.2.2 Problem Analysis -- 6.2.3 Algorithm Design and Complexity Analysis -- 6.2.4 Simulation Results -- 6.3 Updated Caching -- 6.3.1 Age of Information -- 6.3.2 Joint Service Update and Computation Offloading -- 6.3.3 Algorithm Design and Complexity Analysis -- 6.3.3.1 Algorithm Design -- 6.3.3.2 Solving the Static Optimization Problem -- 6.3.4 Simulation Results -- 6.3.4.1 Simulation Setup -- 6.3.4.2 Computation Offloading Cost -- 6.3.4.3 Average AoI of Sensors -- 6.4 Summary -- References -- 7 Edge Resource Provisioning -- 7.1 Introduction -- 7.2 System Model -- 7.2.1 Resource Cost -- 7.2.2 Service Delay -- 7.2.3 Problem Analysis. ; 7.3 Edge Resource Provisioning with On-demand Cloud Instances -- 7.3.1 Optimal Usage of On-demand Cloud Instances -- 7.3.2 Optimal Edge Resource Capacity -- 7.4 Edge Resource Provisioning with Reserved Cloud Instances -- 7.4.1 Problem Analysis -- 7.4.2 Optimal Edge Resource Capacity and Usage of Reserved Cloud Instances -- 7.5 Edge Resource Provisioning with Hybrid Cloud Instances -- 7.6 Performance Analysis -- 7.7 Summary -- References -- 8 Edge Computing for 5G and 5G-based Mobile Edge Computing System -- 8.1 Introduction -- 8.2 5G-based MEC System -- 8.3 Seamless Service Migration in 5G-based Mobile Edge Computing -- 8.3.1 Background -- 8.3.2 Existing Service Migration Solutions -- 8.3.3 Seamless Service Migration Scheme for Immersive Services -- 8.3.4 Summary -- 8.4 Toward High-Profit Edge Server Placement in 5G-based Mobile Edge Computing -- 8.4.1 Background and Motivation -- 8.4.2 System Model and Problem Formulation -- 8.4.2.1 Access Delay -- 8.4.2.2 Energy Consumption -- 8.4.2.3 SLA Model -- 8.4.2.4 Problem Formulation -- 8.4.3 Edge Server Placement -- 8.4.3.1 Encoding Scheme -- 8.4.3.2 Edge Server Placement Algorithm -- 8.4.4 Performance Analysis -- 8.4.4.1 Experiment Setup -- 8.4.4.2 Benchmark Algorithms -- 8.4.4.3 Performance with Varying Maximum Delay -- 8.5 Summary -- References -- 9 Visions of Edge Computing in 6G -- 9.1 Introduction -- 9.2 Orbital Edge Computing -- 9.2.1 Rapid Proliferation of Low Earth Orbit Small Satellites -- 9.2.2 Potentials and Challenges of Orbital Edge Computing -- 9.3 Our Exploration Toward Edge Computing for 6G -- 9.3.1 Service Coverage in Orbital Edge Computing -- 9.3.1.1 Service Placement -- 9.3.1.2 Model of Service Coverage and Robustness -- 9.3.1.3 Problem Formulation -- 9.3.1.4 Algorithm Design -- 9.3.1.5 Simulation Results -- 9.3.2 Cognitive-Driven 6G Core Network Architecture. ; 9.3.2.1 Cognitive Service -- 9.3.2.2 The Architecture of Cognitive Service -- 9.3.3 A First Deployment of Edge Core Network on Satellites -- 9.3.3.1 Motivation -- 9.3.3.2 Architecture -- 9.3.3.3 Evaluation -- 9.3.4 Tiansuan: An Open Satellite-Terrestrial Integrated Platform for 6G -- 9.3.4.1 Overview -- 9.3.4.2 Operation Mechanism -- 9.3.4.3 Potential Spectrum of Experiments -- 9.3.4.4 Case Study -- 9.4 Summary -- References -- 10 Conclusions and Future Directions.
• Titolo autorizzato: 5G Edge Computing
• ISBN: 9789819702138
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910855384903321
• Lo trovi qui: Univ. Federico II