雲端運算與巨量資料分析
Cloud Computing and Big Data Analytics
| 節 | 週四 |
|---|---|
2 09:00–09:50 | 雲端運算與巨量資料分析 ED814 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
[This course will be taught in English.] Cloud Data centers are the backbone infrastructure for tomorrow's information technology. Their advantages are efficient resource provisioning and low operational costs for supporting a wide range of computing needs, be it in business, scientific or mobile/pervasive environments. Because of the rapid growth in user-defined and user-generated programs, applications and files, the range of services provided at data centers will expand tremendously and unpredictably. Particularly, Big Data applications and service present a unique class of challenge s in Cloud. The high volume of mixed workloads and the diversity of services offered render the performance optimization of data centers ever more challenging. Moreover, important optimization criteria, such as scalability, reliability, manageability, power efficiency, area density, operating cost and many more, often are even mutually exclusive to some extent. On top of that, the increasing mobility of users across geographically distributed areas adds another dimension to optimizing big data and cloud performance. The goal of this class is to provide the backgrounds and knowledge to understand and identify suitable strategies to enable effective and scalable performance optimizations. 雲端資料運算中心是未來資訊與運算的骨幹,雲端運算資料中心可以提供運算所需要的資源以及較低的操作成本。在許多使用者自訂應用中,雲端運算資料中心提供的一系列服務正在快速地擴張,尤其是大數據應用及服務中大量的混和資料、繁瑣的運算流程以及對於效能要求的完美更成為雲端資料運算中心的極大挑戰。 因此本課程從雲端運算的觀點出發,教授雲端運算的基礎知識以及雲端資料運算中心的系統架構,讓學生在設計應用時能夠將應用的擴增性和效能考慮進去,同時也介紹大資料分析的基礎演算法,讓學生有能力設計大資料分析的應用在雲端平台中,最後講述一些網路以及安全性,讓學生了解資料安全的重要性。
Python
Lab (29%): 5% each for Labs 1-4, 3% each for Labs 5-7 Homework (35%): 5% for HW#1, 10% each for HW #2, #3, and #4 Quiz (6%): randomly happens, 1 pts each Final project (30%) Bonus (up to 6 pts): class participation
| 週次 | 主題 |
|---|---|
| 第 0 週 | CNN and RNN (HW#3: “Real” prediction) |
| 第 0 週 | Cloud Concepts & Technologies |
| 第 0 週 | Course Syllabus and Course Introduction (HW#1) |
| 第 0 週 | Data Mining: Association Rule and Sequential Pattern Mining (HW#2: SPM) |
| 第 0 週 | Daysoff |
| 第 0 週 | Final Project Presentation |
| 第 0 週 | Hadoop and MapReduce with Lab 3 |
| 第 0 週 | Introduction of Big Data: Challenges and Solutions |
| 第 0 週 | Introduction to Deep Learning |
| 第 0 週 | Lab 6: InfoSphere (2) |
| 第 0 週 | Lab 7: InfoSphere (3) |
| 第 0 週 | Large-scale Machine Learning |
| 第 0 週 | Philosophy and Introduction to Data Mining |
| 第 0 週 | RL with Lab 2: Ping-Pong Balancing |
| 第 0 週 | Spark with Lab 4 (HW #4) |
| 第 0 週 | Spring Break |
| 第 0 週 | Stream Computing with InfoSphere, Lab 5: InfoSphere (1) |
1. Cloud Computing: A Hands-On Approach by Arshdeep Bahga, Vijay Madisetti 2. Data Mining by Aggarwal, Charu C. 3. Learning Spark by Holden Karau, Andy Knowinski, Patrick Wendell & Matei Zaharia
- 地點
- ED-807
- 時間
- TA hour: CD, Wednesday or by appointment @ ED-716 •Wei-Lun Eric Tseng: eric840610.ee02@g2.nctu.edu.tw Office hour: 2 pm - 4 pm (every Tuesday)
- 聯絡方式
- TEL: (03)571-2121#54530 EMAIL: hhshuai@nctu.edu.tw