Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
LEARNING OUTCOMES
After the course the students are able to measure and analyze basic properties of network traffic and draw conclusions on the results. They obtain skills to apply and evaluate statistical methods in processing, analyzing and presenting the measurement data. Students gain understanding of the technical and legal issues related to network measurements and become familiar with the related methods and software tools.
Credits: 5
Schedule: 09.09.2020 - 30.11.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Esa Hyytiä, Markus Peuhkuri
Teacher in charge (applies in this implementation): Esa Hyytiä, Markus Peuhkuri
Contact information for the course (valid 21.08.2020-21.12.2112):
Markus Peuhkuri <markus.peuhkuri@aalto.fi>
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
This course introduces different ways of measuring Internet traffic and provides an overview on tools that can be utilized to analyze the results. Course topics include packet, flow and routing related measurements and their analysis. Also related technical and legal issues are covered. Exercises include measurement data processing and analysis using statistical/mathematical software. In the assignment, data traffic measurements are performed, analysed and reported.
Applies in this implementation:
This course will provide you practical skills to do communication
network measurements. You are introduced with basic tools that allow
you to perform measurements and collect results. Majority of tools
covered are Linux command line tools that are de-facto standards in
network engineering and scientific community.Collected measurement data is then processed, analysed and
presented by applying statistical methods. You will learn
applicability of the methods for each situatuation. Recommended
environments include python and R.Network measurements can include personally identifiable
information (PII). Legal framework and regulation that dicatetes how
data can be collected, processed and stored are discussed.
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
Compulsory: Examination, assignment(s), exercises.
Applies in this implementation:
There are 5 mandatory weekly assignment that provide maximum 30
points. These weekly exercises provide skills needed for the final
assignment.The final assignment contributes 70 points for total 100 points.
Each assignment report must be based on individually collected
measurement data and the report, including figures, must be
individual work. Students are, however, encouraged to co-operate and
help others.
Workload
Valid 01.08.2020-31.07.2022:
Contact hrs 37 h
Independent learning (incl. assignment): 96 hoursApplies in this implementation:
Lectures 24
hoursExcersises 12
hoursSelf-study on
material: 24 hoursWeekly
excersises: 36 hoursFinal assignment:
36 hours
DETAILS
Study Material
Applies in this implementation:
Lecture notes
by Markus Peuhkuri (65 pages)Lecture slides
for other lecturesSupporting
material for assignments
Substitutes for Courses
Valid 01.08.2020-31.07.2022:
S-38.3184
Prerequisites
Valid 01.08.2020-31.07.2022:
Basic probability theory and statistics (e.g., MS-A0510), and basics on internet technology
Registration for Courses
Valid 01.08.2020-31.07.2022:
WebOodi
Applies in this implementation:
Course assumes at least basic level of understanding on Unix-type (e.g. Linux) operating systems and programming. First exercise will be introduction to the tools.
SDG: Sustainable Development Goals
8 Decent Work and Economic Growth
FURTHER INFORMATION
Details on the schedule
Applies in this implementation:
The course has weekly lectures starting from the first period.There are two weekly exercises at the begining of the course. Rest
of exercises are scheduled according to lectures. For the final
assignment there are non-mandatory exercise sessions.