- 移动微服务(构建灵活的普适应用)(英文版)
- - 作者：陈南希|责编:贺瑞君//赵一
  - 出版社：人民邮电
  - ISBN：9787115584021
  - 出版日期：2022/04/01
  - 页数：228
- 售价：59.6

内容大纲
本书聚焦移动微服务的应用层面，讨论普适计算环境下的微服务组合。具体内容包括微服务普适应用的设计概念、边缘或雾计算环境下的微服务部署、改善总体服务可用性的合作式微服务布局，以及移动微服务的一个实现案例GoCoMo，书中还评估了所提供解决方案在多大程度上可以应对普适计算环境下的已知挑战、能多大程度解决学术研究所提出的问题，在总结了服务组合问题后，作者也指出了一些有待进一步研究解决的问题，本书适合边缘计算、移动普适计算、5G网络应用等方面的研究人员、工程师及研究生等人员参考。
作者介绍
陈南希，博士，现任中国科学院上海微系统与信息技术研究所副研究员、课题组组长，中国科学院大学博士生导师。研究领域包括泛在智能、分层算力网络。曾作为核心骨干参与国内外多项物联网项目研发，主持多个通信网络架构方向的项目与课题。
目录
Chapter 1  Introduction
Chapter 2  Design Concepts for Pervasive Applications
  2.1  Motivating Scenario: A Smart Public Space
    2.1.1  Challenges
    2.1.2  Possible Solutions
  2.2  Locating A Provider
    2.2.1  Reactive Discovery
    2.2.2  Proactive Discovery
    2.2.3  Planning-based Composition Announcement
  2.3  Request Routing
    2.3.1  Controlled Flooding
    2.3.2  Directory-based
    2.3.3  Overlay-based
    2.3.4  Dynamic Controlled Flooding
  2.4  Composition Planning
    2.4.1  Open Service Discovery
    2.4.2  Goal-oriented Planning
    2.4.3  Decentralized Flexible Backward Planning
  2.5  Service Binding
    2.5.1  QoS-based Selection
    2.5.2  Adaptable Binding
    2.5.3  On-demand Binding
    2.5.4  Path Reliability-driven Selection
    2.5.5  Bind Microservices on-demand
  2.6  Service Invocation
    2.6.1  Fragments Distribution
    2.6.2  Process Migration Approaches
    2.6.3  Runtime Service Announcement
  2.7  Fault Tolerance
    2.7.1  Preventive Adaptation
    2.7.2  Composition Recovery
    2.7.3  Local Execution Path Maintenance
  2.8  Chapter Summary
Chapter 3  Microservice Deployment in Edge/Fog Computing Environments
  3.1  Edge Computing: Pervasive Applications' New Enabler
  3.2  Features in Edge Computing Environments
    3.2.1  Latency-sensitive
    3.2.2  Mobility is Everywhere
    3.2.3  Openness of Network Systems
    3.2.4  Constantly Changing Environment
    3.2.5  Limited Power Supply
  3.3  Fog as a Service Model
  3.4  Edge/Fog Computing Architecture
  3.5  Fog Node Overlay Network
  3.6  Hierarchical Microservices Management
    3.6.1  Fog Services and Service Composition
    3.6.2  Proxy Fog Nodes
    3.6.3  Seamless Service Invocation
  3.7  Adaptability at Edge
    3.7.1  Monitoring Environmental Changes

    3.7.2  Adaptation Analysis Based on Deep Learning
    3.7.3  Adaptation Planning Based on Reinforcement Learning
    3.7.4  Strategy Execution and Knowledge Base Utilization
    3.7.5  Extension of the MAPE-K Framework
  3.8  Microservice Deployment and Dynamic Redeployment
  3.9  Examples of Pervasive Applications at Edge
    3.9.1  Mobile Video
    3.9.2  Smart Home
    3.9.3  Computational Offloading
  3.10  Open Issues to Edge-enabled Pervasive Applications
    3.10.1  End-to-end Security
    3.10.2  Distributed Runtime Management
    3.10.3  Scalability and Reconfigurability
    3.10.4  Predictive Fault Tolerance
    3.10.5  Intelligent Edge Computing for 6G
  3.11  Chapter Summary
Chapter 4  Microservices Composition Model
  4.1  Microservice Model
  4.2  Dynamic Goal-driven Composition Planning
    4.2.1  Local Service Planning
    4.2.2  Complex Service Flows
  4.3  Heuristic Service Discovery
  4.4  Execution Fragments Selection and Invocation
    4.4.1  Microservice Composite Selection and Invocation
    4.4.2  Service Execution and Guidepost Adaptation
  4.5  Discussions
    4.5.1  Quantitative Analysis
    4.5.2  Service Flows
    4.5.3  Privacy and Security
    4.5.4  Semantic Matchmaking
    4.5.5  High Composition Demand
  4.6  Chapter Summary
Chapter 5  Cooperative Microservices Provisioning
  5.1  Cooperative Caching and Selfish Caching
    5.1.1  Social Behaviours in Caching
    5.1.2  Social Selfishness of Service Providers
  5.2  Local Optimal Caching Algorithm with Social Selfishness
  5.3  Cooperative Devices
  5.4  Social Selfishness-based Utility
    5.4.1  Access Admission Mechanism
    5.4.2  Social Group Utility Mechanism
  5.5  Service Deployment and Provisioning Game
  5.6  Optimal Local Service Deployment
  5.7  Chapter Summary
Chapter 6  Implementation I: Service Middleware
  6.1  Service Composition Architecture
  6.2  Client and Provider
    6.2.1  Client Engine
    6.2.2  Microservices Provider
  6.3  Routing Controller

  6.4  Guidepost Manager
    6.4.1  Adapting a Guidepost
    6.4.2  Guidepost Data in Service Execution
  6.5  Message Helper
  6.6  Prototypes
    6.6.1  Prototype on Android
    6.6.2  Prototype on ns
  6.7  Implementation Summary
Chapter 7  Implementation II: Artificial Intelligence Services
  7.1  Service Provisioning Frameworks
    7.1.1  Spring Cloud
    7.1.2  Service Configuration
    7.1.3  Service Registration at Edge
    7.1.4  Service Gateway
  7.2  Deploy AI Models
    7.2.1  Packed as a Microservice
    7.2.2  Microservice Deployment
    7.2.3  Platforms for AI Services
  7.3  Challenges for AI-based Services Composition
    7.3.1  Feature Heterogeneity
    7.3.2  High-dimensional Data
    7.3.3  Dynamic Raw Data
  7.4  Implementation Summary
Chapter 8  Evaluation
  8.1  Evaluation Methods and Criteria
  8.2  Prototype Case Study
    8.2.1  Case Study Configurations
    8.2.2  Samples and Results
  8.3  Simulation Studies
    8.3.1  Environment Configurations
    8.3.2  Baseline Approach
    8.3.3  Simulation Results and Analysis
  8.4  Evaluation Summary
    8.4.1  Service Composition
    8.4.2  Cooperative Service Provisioning
Chapter 9  Discussions and Conclusions
Appendix A  Further Implementation Detail: Prototypes
  A.1  GoCoMo App
  A.2  GoCoMo-ns3
Appendix B  Evaluation Results' Validity
  B.1  CoopC and GoCoMo's Service Discovery Time
  B.2  CoopC and GoCoMo's Service Discovery Traffic
  B.3  CoopC and GoCoMo's Response Time
  B.4  CoopC and GoCoMo's Composition Traffic
Appendix C  Glossary of Key Terms
Bibliography

内容大纲

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