- 2011 Fall Semester, Wednesday (14:45-16:15)
- Faculty: Kenjiro Cho (kjc at sfc.keio.ac.jp)
- TA: Yohei Kuga (sora at sfc.wide.ad.jp)
- SA: Midori Kato (katoon at ht.sfc.keio.ac.jp), Ryo Nakamura (upa at sfc.wide.ad.jp)
- Class home page: http://web.sfc.keio.ac.jp/~kjc/classes/sfc2011f-measurement/
- Class support mail (Faculty, TA and SA): imda at sfc.wide.ad.jp

Now that the Internet has become a social infrastructure, it becomes increasingly important to understand the current usage and behavior of the Internet and predict the future, not only for technical aspects but also for investment decisions and policy making.

However, it is challenging to grasp the Internet that is gigantic and complex systems; while it is not realistic to perform large-scale measurement covering the entire Internet, it is often the case that traditional sampling methods cannot be applied. Moreover, there are various technical, social, economical, and legal constraints, and we need to solve problems under these constraints.

In this class, you will learn about the overview of Internet measurement and large-scale data analysis, and basic skills for the forthcoming information society to obtain new knowledge from massive information.

In this class, you will learn about Internet measurement and data analysis methods, to obtain knowledge and understanding of networking technologies and large-scale data analysis. Each class will provide specific topics where you will learn problems, constraints, and solutions. At the same time, you will learn technical and theoretical backgrounds of the topics such as networking technologies, statistics, and algorithms. Each class consists of a lecture, and exercises on data analysis.

The lecture slide materials will be provided online.

ruby: http://www.ruby-lang.org/ gnuplot: http://gnuplot.info/ [1] Mark Crovella and Balachander Krishnamurthy. Internet measurement: infrastructure, traffic, and applications. Wiley, 2006. [2] Antonio Nucci and Konstantina Papagiannaki. Design, Measurement and Management of Large-Scale IP Networks: Bridging the Gap Between Theory and Practice. Cambridge University Press, 2008. [3] Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Introduction to Data Mining. Addison Wesley, 2006. [4] Raj Jain. The art of computer systems performance analysis. Wiley, 1991.

2 assignments and a final report.

The prerequisites for the class are basic programming skills and basic knowledge about statistics.

In the exercises and assignments, you will need to write programs to process large data sets, using the Ruby scripting language and the Gnuplot plotting tool. To understand the theoretical aspects, you will need basic knowledge about algebra and statistics. However, the focus of the class is to understand how mathematics is used for engineering applications.

$Date: 2012/01/10 14:12:00 $