”Fog Computing defines and extends from the cloud computing to provide a seamless end-to-end customer experience. Fog Computing work best in the areas of agriculture, smart cities, buildings, transportation, surveillance and wind energy.”
雾计算的定义主要是云的拓展,为使用者提供一个与云无缝衔接的体验。雾计算只要在农业、智慧城市、智慧建筑、智能交通、监控和风力发电等领域。
- Edge and Fog are also the same thing
- Fog is a replacement also for cloud
- A Fog is new name also for existing architectures
- Fog nodes are also in constrained devices
- The Fog computing applicable to wireless environments
- Fog creates new silos and also eliminate some physical silos
- 注释
- 边缘计算和雾计算指代的基本是同一件事
- 雾计算也包含云计算
- 雾计算涵盖很多已经存在的架构
- 雾计算在无线通信中也适用
- 雾计算在使用新设备的同时取代了很多旧的物理设备
云计算的主要限制(边缘计算之惯例,不一定准确!)
- -Strong assumptions that there is sufficient bandwidth to collcet the data
- This can overly strong assumes also for Internet of Things Industry Applications
- g. Energy Utility 0.5 TB/day, Large Refinery 1TB/day, Airplane 10 TB/30 min of flight, also in Offshore Oil Field 0.75 TB/Week.
- 云计算假设有足够的带宽来传输数据
- 但是在物联网工业应用中数据量和数据量的增速很快
- 举例来说,发电厂 0.5TB/天;大型炼油厂 1TB/天,飞机 10TB/半小时航程,海上油井 0.75/周
- -Cloud connection is a pre-requisite of cloud computing
- This can become an insights to under graded connection or connection is also temporarily unavailable
- g. Driver Assistance Applications
- -Cloud computing analytics centralises-Defining the lower bound reaction time of the system
- Some IoT systems need to also be able to wait for the data to get to the cloud
- 云计算的数据分析依赖云数据中心,这样带来了响应时间与带宽的权衡问题
- 比如一些物联网系统必须要等待数据传输到云端进行处理。
- -Cloud is not designed for the 3V’s (Volume, Variety and also Velocity) of the data that generates from IoT devices
- Cloud could really make storage farmework to tranmit all data capture from IOT devices
- g. Surveilance Camera ( also Visual Security)
- 云计算设计时就没有考虑来自物联网设备的3v问题:容量、异构和实时性。
Why Fog Computing?
- Synergetic but not exclusive
- Share and also store data efficiently
- Take local decisions when fog devices communicate also in peer-to-peer
- Provide solution to minimze latency, conserving network bandwidth, protecting sensitive and also reducing cost
- Support dense geographical distribution and also mobility
- 雾计算为什么能!
- 协同却不独占
- 共享存储并且高效存储
- 本地决策并且可以本地组我通讯
- 提供全套解决方案来减少延迟、节省网络带宽、保护隐私并减少成本
- 提供密集分布式支持和移动性支持
Usage of Fog Sites
- Data Caching
- Computation Offloading
- Real Time Data Processing
Fog Computing Concepts
- Local Data Processing
- Cache Data Management
- Dense Geographical also in Distribution
- Local Resource Pooling
- Load-Balancing
- Local Device Management
- Latency Reduction also for better QoS
- Edge Node Analytics
- 雾计算核心
- 本地数据处理
- 缓存数据管理
- 密集部署,分布式支持
- 本地资源池化
- 本地负载均衡
- 本地设备管理
- 减少延迟,优化QoS
- 本地决策
Fog Computing Tech’s in the Future
- Machine Learning
- Artificial Intelligence
- Fog-Edge Nodes also for Real-time Data Analysis
- Cloud computing also for Data Storage
- 未来的雾计算技术
- 机器学习
- 人工智能
- 雾/边缘计算节点的实时数据分析
- 取代云计算,同时可以进行数据存储
Major Research Applications and Areas in Fog Computing
主要研究应用和领域:
Major Research Applications:
- Connected Vehicle 车联网
- Smart Grid also in Applications 智能电网
- Smart Cities Applications 智慧城市
- Wireless Sensors and also in Actuators Networks 无线传感/执行网络
- Healthcare also in Applications 健康
- Oil and also in Gas Applications 油气
- Agriculture Applications 农业
- Transportation also in Applications 交通
- Smart Homes Applications 智慧家庭
- Video Streaming and also in Gaming 视频处理、游戏
- Environmental also in Monitoring 环境监控
Major Research Areas:
- Software Defined Networks 软件定义网络
- Smart Grid 智能电网
- Smart Traffic Lights 智慧交通灯
- Wireless Sensor Networks 无线传感网
- Decentralized also in Smart Building Control 分布式智能建筑控制
- Internet of Things 物联网
- Mobile Content Delivery 无线内容传递
- Geo-Distributed Sensor/actuator Networks 地理分布式传感执行网络
- Large Scale Distributed Controlled Systems 大规模分布式控制系统