Programplaner og emneplaner - Student
PENG9630 Internet Architecture and Measurements Course description
- Course name in Norwegian
- Internet Architecture and Measurements
- Study programme
-
PhD Programme in Engineering Science
- Weight
- 10.0 ECTS
- Year of study
- 2022/2023
- Programme description
- Course history
-
Introduction
Se emneplan under for beskrivelse av studiet.
Recommended preliminary courses
Bachelor's or master's degree in engineering science or related fields.
Required preliminary courses
None.
Learning outcomes
Knowledge
On successful completion of the course, the student:
- has an overview of the different elements that comprise the architecture of today’s internet.
- has a good understanding about the approaches for conducting internet measurements and the latest advances in this field.
- be familiar of a broad set of tools that can help analyzing Internet measurments. Of a particular relevance here are tools that originate in other disciplines like Machine Learning and Statisitcal Physics. This will not only expand the available toolset but also increases the potential for interdisciplinory collaboration going forward.
Skills
On successful completion of the course, the student can:
- plan and carry out state-of-the-art measurement tasks
- can formulate research questions on the robustness and performance of operational networks, and design measurements for evaluating these questions.
- will have a general practical understanding of how different parts of the internet's architecture interplay to offer a performant end-to-end service.
General competence
On successful completion of the course, the student can:
- participate in debates and present aspects of his/her expertise in a way that promotes such discussions.
- drive innovation
Content
The course consists of three modules.
In the first module, the course staff and guest lecturers will provide a high-level overview of different parts of the internet's architecture.
The second part is a set of practical exercises that are designed to match the topics discussed in the first module.
The third module will consist of a set of seminars, where students elaborate on different parts of the architecture and how they can be assessed and monitored.
Teaching and learning methods
Module 1 will take the form of lectures. Module 2 will take the form of lab and homework assignments. Module 3 will take the form of seminars. In module 3, the student will present a case to the other students. We will also invite guest lecturers from research groups that focuses on machine learning and network science to introduce the students to potential tools and analysis methods.
Practical training
The students will participate in lab experiments to explore how once can measure various aspects of internet's robustness and performance. The students will write a summary of one of the tools that were introduced in the lab and discuss its benefits and limitations.
Course requirements
None.
Assessment
Both the presentation of the case in Module 3 of the course and the tool summary document in;the practical training part the course will form basis of assessment.
Both exams must be passed in order to pass the course.
;
The oral presentation cannot be appealed.
Permitted exam materials and equipment
All aids are permitted.
Grading scale
Pass or fail.
Examiners
The presentation will be assessed by the course leader, whereas the tool summary document will be assessed by the course leader together with an additional examiner. External examiner is used periodically.