移动微服务 构建灵活的普适应用 陈南希 著 专业科技 文轩网

Chapter 1 Introduction 1
Chapter 2 Design Concepts for Pervasive Applications 5
2 1 Motivating Scenario:A Smart Public Space 6
2 1 1 Challenges 9
2 1 2 Possible Solutions 11
2 2 Locating A Provider 14
2 2 1 Reactive Discovery 15
2 2 2 Proactive Discovery 16
2 2 3 Planning-based Composition Announcement 18
2 3 Request Routing 21
2 3 1 Controlled Flooding 22
2 3 2 Directory-based 23
2 3 3 Overlay-based 25
2 3 4 Dynamic Controlled Flooding 26
2 4 Composition Planning 28
2 4 1 Open Service Discovery 28
2 4 2 Goal-oriented Planning 30
2 4 3 Decentralized Flexible Backward Planning 31
2 5 Service Binding 32
2 5 1 QoS-based Selection 33
2 5 2 Adaptable Binding 34
2 5 3 On-demand Binding 35
2 5 4 Path Reliability-driven Selection 35
2 5 5 Bind Microservices on-demand 36
2 6 Service Invocation 37
2 6 1 Fragments Distribution 37
2 6 2 Process Migration Approaches 38
2 6 3 Runtime Service Announcement 39
2 7 Fault Tolerance 40
2 7 1 Preventive Adaptation 40
2 7 2 Composition Recovery 42
2 7 3 Local Execution Path Maintenance 43
2 8 Chapter Summary 44
Chapter 3 Microservice Deployment in Edge/Fog Computing Environments 45
3 1 Edge Computing:Pervasive Applications' New Enabler 46
3 2 Features in Edge Computing Environments 48
3 2 1 Latency-sensitive 49
3 2 2 Mobility is Everywhere 49
3 2 3 Openness of Network Systems 50
3 2 4 Constantly Changing Environment 50
3 2 5 Limited Power Supply 51
3 3 Fog as a Service Model 51
3 4 Edge/Fog Computing Architecture 54
3 5 Fog Node Overlay Network 56
3 6 Hierarchical Microservices Management 57
3 6 1 Fog Services and Service Composition 57
3 6 2 Proxy Fog Nodes 58
3 6 3 Seamless Service Invocation 59
3 7 Adaptability at Edge 59
3 7 1 Monitoring Environmental Changes 61
3 7 2 Adaptation Analysis Based on Deep Learning 62
3 7 3 Adaptation Planning Based on Reinforcement Learning 63
3 7 4 Strategy Execution and Knowledge Base Utilization 63
3 7 5 Extension of the MAPE-K Framework 64
3 8 Microservice Deployment and Dynamic Redeployment 65
3 9 Examples of Pervasive Applications at Edge 67
3 9 1 Mobile Video 67
3 9 2 Smart Home 68
3 9 3 Computational Offloading 69
3 10 Open Issues to Edge-enabled Pervasive Applications 71
3 10 1 End-to-end Security 71
3 10 2 Distributed Runtime Management 71
3 10 3 Scalability and Reconfigurability 72
3 10 4 Predictive Fault Tolerance 73
3 10 5 Intelligent Edge Computing for 6G 73
3 11 Chapter Summary 74
Chapter 4 Microservices Composition Model 75
4 1 Microservice Model 78
4 2 Dynamic Goal-driven Composition Planning 79
4 2 1 Local Service Planning 84
4 2 2 Complex Service Flows 88
4 3 Heuristic Service Discovery 90
4 4 Execution Fragments Selection and Invocation 93
4 4 1 Microservice Composite Selection and Invocation 94
4 4 2 Service Execution and Guidepost Adaptation 95
4 5 Discussions 97
4 5 1 Quantitative Analysis 97
4 5 2 Service Flows 99
4 5 3 Privacy and Security 99
4 5 4 Semantic Matchmaking 100
4 5 5 High Composition Demand 100
4 6 Chapter Summary 101
Chapter 5 Cooperative Microservices Provisioning 103
5 1 Cooperative Caching and Selfish Caching 105
5 1 1 Social Behaviours in Caching 105
5 1 2 Social Selfishness of Service Providers 106
5 2 Local Optimal Caching Algorithm with Social Selfishness 106
5 3 Cooperative Devices 107
5 4 Social Selfishness-based Utility 108
5 4 1 Access Admission Mechanism 109
5 4 2 Social Group Utility Mechanism 110
5 5 Service Deployment and Provisioning Game 112
5 6 Optimal Local Service Deployment 112
5 7 Chapter Summary 116
Chapter 6 Implementation I:Service Middleware 117
6 1 Service Composition Architecture 118
6 2 Client and Provider 121
6 2 1 Client Engine 121
6 2 2 Microservices Provider 123
6 3 Routing Controller 125
6 4 Guidepost Manager 125
6 4 1 Adapting a Guidepost 126
6 4 2 Guidepost Data in Service Execution 128
6 5 Message Helper 128
6 6 Prototypes 129
6 6 1 Prototype on Android 129
6 6 2 Prototype on ns-3 131
6 7 Implementation Summary 132
Chapter 7 Implementation II:Artificial Intelligence Services 133
7 1 Service Provisioning Frameworks 134
7 1 1 Spring Cloud 135
7 1 2 Service Configuration 136
7 1 3 Service Registration at Edge 137
7 1 4 Service Gateway 139
7 2 Deploy AI Models 141
7 2 1 Packed as a Microservice 142
7 2 2 Microservice Deployment 147
7 2 3 Platforms for AI Services 150
7 3 Challenges for AI-based Services Composition 151
7 3 1 Feature Heterogeneity 151
7 3 2 High-dimensional Data 152
7 3 3 Dynamic Raw Data 152
7 4 Implementation Summary 152
Chapter 8 Evaluation 155
8 1 Evaluation Methods and Criteria 156
8 2 Prototype Case Study 158
8 2 1 Case Study Configurations 159
8 2 2 Samples and Results 161
8 3 Simulation Studies 166
8 3 1 Environment Configurations 166
8 3 2 Baseline Approach 171
8 3 3 Simulation Results and Analysis 172
8 4 Evaluation Summary 188
8 4 1 Service Composition 188
8 4 2 Cooperative Service Provisioning 189
Chapter 9 Discussions and Conclusions 191
Appendix A Further Implementation Detail:Prototypes 195
A 1 GoCoMo App 196
A 2 GoCoMo-ns3 199
Appendix B Evaluation Results' Validity 201
B 1 CoopC and GoCoMo's Service Discovery Time 202
B 2 CoopC and GoCoMo's Service Discovery Traffic 203
B 3 CoopC and GoCoMo's Response Time 204
B 4 CoopC and GoCoMo's Composition Traffic 205
Appendix C Glossary of Key Terms 207
Bibliography 211