雲霧計算與大數據分析
Cloud Fog Computing and Big Data Analytics
| 節 | 週二 | 週五 |
|---|---|---|
3 10:10–11:00 | 雲霧計算與大數據分析 ED202 2 節連堂 | |
4 11:10–12:00 | ||
7 15:30–16:20 | 雲霧計算與大數據分析 ED202 |
* 根據陽明交大上課時間表所列
為了實現「雲霧計算」的雙管齊下,微軟已和思科聯手,將微軟的Azure物聯網雲平台與思科的Fog Data Service整合在一起。雲霧計算廣義來說指的是基於網際網路之視需要的分散式計算。即使用者透過網際網路視需要存取在雲或霧端的(虛擬)資源(如計算、記憶體、網路、硬碟,軟體及資料等)。本課程旨在探討雲霧計算環境所牽涉到的原理,機制及架構,也將討論雲及霧端計算與大數據分析、物聯網、軟體定義網路及網路功能虛擬化的緊密關係及相關議題。透過雲霧端計?與大數據分析的完美的互補結合,分析來自物聯網的大數據,獲取智慧,並回饋給物聯網應用,使其更有智慧及更人性化。此外,本課程理論與實務並重,透過多個動手做的實驗(作業),同學可以獲得雲及霧端計算與大數據分析的實務經驗,此有助於對雲及霧端與大數據分析技術及架構有更深入的瞭解。 To achieve “Cloud Fog Computing,” Microsoft and Cisco enable Azure IoT Cloud Platform to connect to Cisco Fog Data Service. Cloud fog computing is an internet-based on demand computing that users can access cloud or fog resources (CPU, memory, network, storage, applications/services, and data, etc.) on demand. This course aims at studying principles, mechanisms, and architecture involved in integrated cloud and fog computing environments. The close relationships and associated issues among cloud fog computing, bid data analytics, IoT (internet of things), SDN (software defined networks), and NFV (network function virtualization) are discussed. Integrating cloud fog computing and big data analytics, which is a perfect complementary combination, to analyze big data obtained from IoT applications may discover intelligence (useful patterns and insights) to make these applications smart and to achieve good user experiences. In addition, this course emphasizes both theory and practice of cloud fog computing and big data analytics. By conducting several hands-on experiments (homework), students have opportunities to gain practical experiences of cloud fog computing and big data analytics so as to benefit from deep understanding of related techniques and frameworks.
Having programming experience
https://e3.nctu.edu.tw
評分方式 Grading Policy: 作業 Homework 30% 期末考 Final Exam 40% 期末創意專題報告 Final Creative Project 30% 課程參與 Class Participation 10% (extra, optional)
| 週次 | 主題 |
|---|---|
| 第 1 週 | Chapter 1: Introduction to Cloud Fog Computing and Big Data Analytics |
| 第 2 週 | Chapter 1: Introduction to Cloud Fog Computing and Big Data Analytics (cont.) |
| 第 3 週 | Chapter 2: Infrastructure as a Service (IaaS) |
| 第 4 週 | Chapter 2a: Tools and Technologies for Building Clouds and Fogs |
| 第 5 週 | Chapter 3: Platform as a Service (PaaS) |
| 第 6 週 | Chapter 4: Cloud Computing Applications |
| 第 7 週 | Chapter 4a: Fog/Edge Computing Applications |
| 第 8 週 | Chapter 5: Fog Computing |
| 第 9 週 | Chapter 5a: Edge Computing |
| 第 10 週 | Chapter 6: Big Data Analytics and Internet of Things (IoT) |
| 第 11 週 | Chapter 7: Overview of Machine Learning Algorithms |
| 第 12 週 | Chapter 7: Overview of Machine Learning Algorithms (cont.) |
| 第 13 週 | Chapter 8: Big Data Analytics for the Cloud/Fog Applications |
| 第 14 週 | Chapter 8: Big Data Analytics for the Cloud/Fog Applications (cont.) |
| 第 15 週 | Chapter 9: Deep Learning for IoT Big Data and Streaming Analytics |
| 第 16 週 | Chapter 10: Cloud Fog Computing and Big Data Analytics Perspective |
| 第 17 週 | Creative Project Presentation |
| 第 18 週 | Final exam |
參考教材 Reference Materials 1. "Big-Data Analytics for Cloud, IoT, Cognitive Computing, Kai Hwang and Min Chen, Wiley, 2017. 2."Cloud Computing for Machine Learning and Cognitive Applications, Kai Hwang, The MIT Press, 2017. 3. "Cloud Computing: A Hands-on Approach," A. Bahga and V. Madisetti, 2014. 4. Selected IEEE/ACM journal/conference papers.
- 地點
- EC332A
- 時間
- 5GH
- 聯絡方式
- kwang@cs.nctu.edu.tw or 5131363 (31363)