• FOG COMPUTING研究中的初级概念


    ”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
        •   云计算必须与云相连接
          •   但是在一些场景下未建立连接或者链接会是不是中断
            •   比如无人驾驶
    • -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。
          •   云端很难胜任所有来自物联网设备采集数据的存储
            •   比如监控摄像机和视频安保心疼

    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
      •   雾计算节点的用途
        •   数据缓存
        • 计算卸载
        • 实时数据处理

    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
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  • 原文地址:https://www.cnblogs.com/txhan/p/13204637.html
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