FOG COMPUTING研究中的初級概念

時間 2020-06-29

標籤 fog computing 研究初級概念简体版

原文原文鏈接

」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.」node

霧計算的定義主要是雲的拓展，爲使用者提供一個與雲無縫銜接的體驗。霧計算只要在農業、智慧城市、智慧建築、智能交通、監控和風力發電等領域。react

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
- 註釋
  - 邊緣計算和霧計算指代的基本是同一件事
  - 霧計算也包含雲計算
  - 霧計算涵蓋不少已經存在的架構
  - 霧計算在無線通訊中也適用
  - 霧計算在使用新設備的同時取代了不少舊的物理設備

雲計算的主要限制（邊緣計算之慣例，不必定準確！）緩存

-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.
  - 　　雲計算假設有足夠的帶寬來傳輸數據
    - 　　可是在物聯網工業應用中數據量和數據量的增速很快
      - 　　舉例來講，發電廠 0.5TB/天；大型煉油廠 1TB/天，飛機 10TB/半小時航程，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
  - 　　雲計算必須與雲相鏈接
    - 　　可是在一些場景下未創建鏈接或者連接會是否是中斷
      - 　　好比無人駕駛
-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
  - 　　雲計算的數據分析依賴雲數據中心，這樣帶來了響應時間與帶寬的權衡問題
    - 　　好比一些物聯網系統必需要等待數據傳輸到雲端進行處理。
-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)
  - 　　雲計算設計時就沒有考慮來自物聯網設備的3v問題：容量、異構和實時性。
    - 　　雲端很難勝任全部來自物聯網設備採集數據的存儲
      - 　　好比監控攝像機和視頻安保心疼

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
- 　　霧計算爲何能！
  - 　　協同卻不獨佔
  - 共享存儲而且高效存儲
  - 本地決策而且能夠本地組我通信
  - 提供全套解決方案來減小延遲、節省網絡帶寬、保護隱私並減小成本
  - 提供密集分佈式支持和移動性支持

Usage of Fog Sites

Data Caching
Computation Offloading
Real Time Data Processing
- 　　霧計算節點的用途
  - 　　數據緩存
  - 計算卸載
  - 實時數據處理

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
- 　　霧計算核心
  - 　　本地數據處理
  - 緩存數據管理
  - 密集部署，分佈式支持
  - 本地資源池化
  - 本地負載均衡
  - 本地設備管理
  - 減小延遲，優化QoS
  - 本地決策

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
- 　　將來的霧計算技術
  - 　　機器學習
  - 人工智能
  - 霧/邊緣計算節點的實時數據分析
  - 取代雲計算，同時能夠進行數據存儲