Advanced Network Management-spring2015

Announcements


  • Jun. 10: Alternative homework due date is 5pm June 25th.
  • Jun. 10: Demo & presentation due date is June 17th.
  • Jun. 10: Technical report due date is 5pm June 15th.
  • Mar. 10: Project proposal due date is postponed to April 15th.
  • Mar. 2: Each student please send your email address to [email protected], with subject line “ANM student”.
  • Mar. 2: Website is online!

General Information


Course

Class Time

9:50am-12:15pm Wednesday

Room
Six-Jiao 6B-311

Important Dates
Project Proposal Due: April 15th.
Technical Report Due: 5pm June 15th.
Demo & Presentation: June 17th.

 

Personnel

Associate Professor

Department of Computer Science and Technology

Tsinghua University, Beijing, China 10084

+86(10)62792837

Office: East Main Building 9-319

Mail: [email protected]

Ph.D. Student

Department of Computer Science and Technology

Tsinghua University, Beijing, China 10084

Mail:  [email protected]

 

 

 

 

Course Description


Course Number
80240663

Credit
3

Required text
None.

Reference texts
Computer Networking: Top-Down Approach (6th Edition)
by James F. Kurose, Keith W. Ross.
Statistical Data Mining Tutorials
by Andrew Moore.
Learning From Data
by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin.
Time Series Analysis: Forecasting and Control (4th Edition)
by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel.

Prerequisites
Familiar with at least one programming language. Undergraduate Computer Networks Course.

Grade
Your grade is based on your following performance:
Technical Report: 20%. Due 5pm June 15.
Demo & Presentation: 50%. During the Lecture on June 17.
Homework: 30%. Due 9:50am Wednesday before each of six lectures you picked.
Homework
Our homework format is meant to help the students read the research papers with critical eyes. Each student is required to write critiques for 6 papers in total throughout semester. For each of the twelve lectures (weeks 2,3,5,6,7,8,10,11,12,13,14,15), the instructor will post a few papers on the website by 5pm on the Friday before the lecture. Each student can pick by yourself AT MOST ONE paper out of the papers for each lecture. For each picked paper, based your own understanding, you need to write (in your own words) three contributions of the paper, and three limitations of the paper, and send the critique to our TA via email BEFORE the lecture time (9:50am) of your picked paper. Late submission does not count as submissions
Alternative Homework
Project
Students form project team one or two students. Project proposal is due on April 15th during the lecture, during which each team has 5 minutes to give a talk (with slides) to talk about team members, project idea, the basic approach, expected results, and schedule. A team can pick a topic from the below list of project ideas, or propose a topic by yourself. In either case, the team needs to talk to the instructor and/or the TA about your draft proposal BEFORE the April 15th class presentation. Each team is required to submit a project technical report (PDF format) by 5pm on June 15th. On June 17th during our lecture time, each team is expected to give another (and longer) talk about your finished project, and give a live demo of your project during the talk.

 

 

Syllabus


This course is a graduate course and is primarily project-oriented. It aims to teach students how to build REAL systems that measure the REAL data from the networks and services, process the using Big Data techniques such as Machine Learning, and solve their REAL performance and security problems.

Through case studies based on recent research papers in top network conferences, this course will cover the latest research progress in network management in these areas: measurement, anomaly detection, diagnosis, and mitigation. Along the way, we will also briefly review techniques that have broader applications more than just network management, such as time series analysis, association rule mining, and machine learning.

This course focuses on how to improve the performance of Mobile Internet:

  • Targeted Services: Web-based Services such as search engine, online shopping and social networking; Video Streaming Services.
  • Targeted Networks: Enterprise WiFi Network, Residential WiFi & Broadband Networks, Cellular Networks, and Data Center Networks.
  • Targeted Devices: Smart Phones.

 

Lectures


Week Date Topic Slides&Reading lists
1 Mar 4 Course Introduction & Network Basics  week01
2 Mar 11 Web-based services  week02
3 Mar 18 Web-based services  week03
4 Mar 25 No class
5 Apr 1 Web-based services  week05
6 Apr 8 Video streaming  week06
7 Apr 15 Video streaming (Project proposal Due)  week07
8 Apr 22 WiFi  week08
9 Apr 29 No class
10 May 6 Residential WiFi week10
11 May 13 EWLAN week11
12 May 20 Smart phone 1 week12
13 May 27 Smart phone 2 week13
14 Jun 3 Anomaly detection week14
15 Jun 10 Troubleshooting week15
16 Jun 17 Project Presentation (Project Report Due: Jun 15th 5pm)

Related Links


  • Conferences

SIGCOMM     MobiCom     Mobisys     IMC     CoNEXT     NSDI      USENIX Security   UbiComp

  • Tools and Resources

Wikipedia     R     WEKA     Scikit-learn     Internet [email protected]     Correlation Tests     Matlab
Sound Measurement     Longman Dictionary of American English     Association Rule Mining(Accessible outside China)
CollectionKeywords     CRAWDAD

  • On reading and writting papers

Tenses in Writing     Efficient Reading     The Elements of Style     Style: Lessons in Clarity and Grace
How to Write and Publish a Scientific Paper     Bad Career     How to read a paper     Outside the box     Greatresearch     Accepted at SIGCOMM

 


 

 

 

 

 
 
 
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