IK2219 Performance Evaluation for Network Engineering 7.5 credits
This course has been discontinued.
Last planned examination: Spring 2020
Decision to discontinue this course:
No information insertedContent and learning outcomes
Course contents
This course focuses on broadly applicable methodologies in the field of communication networks, putting special emphasis upon the evaluation of performance therein. A distinctive feature of this course lies in its combination of questions in everyday life and practical problems in communication networks with selected topics in methodologies. In order to help students to gain a better understanding of the methodologies and its applications, the course also contains a set of homework assignments and a project. The textbook can be referred to throughout the academic and industrial careers of students for tackling out various problems in both fields.
§ Content of Lectures
In order to achieve the abovementioned learning outcomes, there are about 11-12 lectures (2 hours) primarily based on the main textbook. The lectures will cover various topics in performance evaluation of communication networks: Summarizing Data (Topic 1), Model Fitting (Topic 2), Tests (Topic 3), Discrete Event Simulation (Topic 4), Palm Calculus (Topic 5), Queuing Theory for Dummies (Topic 6).
§ Review Question Session
Each group of students will present how to solve a few (1-3) selected review questions within a short time (no more than 5 minutes). All review questions and their complete solutions for each topic will be provided by the examiner.
§ Homework Assignments
Introductory Performance Data Summarizations, Random Waypoint Simulation, Queuing Theory from Palm Viewpoint, Web Server Simulation (Queuing Network)
§ Project Assignment (Group work)
Each group will define a performance evaluation problem in their thesis projects or ongoing research projects and apply methodologies in the course to analyse the problem. If it is impossible for a group to find an appropriate problem, the group will give a talk about a topic or a research paper closely related to performance evaluation in networking.
Intended learning outcomes
This course covers a practical collection of theories and techniques which have been widely applied in the field of communication networking over the past decades. It is no surprise that the subject matter stretches over multiple theories. Upon completion of the course, students are expected to learn how to:
Useful Modern Statistics (Chapters 1-4)
- Compute confidence interval and prediction interval
- Fit a suitable model or curve to your measurement data
- Make assertions on your model in a mathematical manner
Basic Theory for Simulation (Chapter 6)
- Internalize theoretical framework in order to run simulations correctly
Palm Calculus: the Importance of Viewpoints (Chapter 7)
- Explain why other queues are always faster when you are shopping
Queuing Theory: Through the Lens of Palm Calculus (Chapter 8)
- Comprehend useful topics in queuing theory from a fresh angle
Throughout the course, students will learn how to utilize various software packages in conducting simulations and validating analyses.
Literature and preparations
Specific prerequisites
Recommended prerequisites
Required: Basic knowledge in probability (e.g., taking expectation and conditional probability).
Recommended: Basic knowledge in communication networks, e.g., IK1550, IK1551, IK2215, IK2217.
Equipment
Literature
Jean-Yves Le Boudec, “Performance Evaluation of Computer and Communication Systems”, 2010, 1st Edition, EPFL Press. ISBN: 978-2-940222-40-7.
The textbook was published in 2010 by EPFL Press. A free pdf version is available online at http://perfeval.epfl.ch/ by courtesy of EPFL Press.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- PRO1 - Project Assignment, 2.5 credits, grading scale: A, B, C, D, E, FX, F
- TEN1 - Exam, 2.5 credits, grading scale: A, B, C, D, E, FX, F
- UPG1 - Home Assignment, 2.5 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Homework Assignments: 2.5 hp, Project Assignment: 2.5 hp, Written Final Exam: 2.5 hp, Grade scale: A-F
Other requirements for final grade
The final grade is based on the weighted average of the four examination parts when each part has received a passing grade: review question session (7%), homework assignment (35%), project assignment (28%), and final written exam (30%).
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
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Main field of study
Education cycle
Add-on studies
Contact
Supplementary information
Will this course come useful to you?
One of the artistic aspects of performance evaluation lies in that seemingly trivial questions in everyday life are directly related to those encountered in the field. For instance, the following questions will turn out to be not only exquisitely relevant to engineering problems in networking but also crucial for avoiding common repetitious pitfalls in the field:
“Why do we feel that we wait relatively longer than others when shopping in ICA or COOP?” (Palm Calculus)
“Can we validate the ad for a ski resort, ‘capacity doubled, waiting time halved’?” (Queuing Theory)
Some more specific problems you may encounter while conducting research or development are as follows:
“If the data is not normal, what kind of method can be used for computing a confidence interval for an arbitrary performance metric?” (Confidence Interval)
“It is well-known that determining whether truncated data follow power-law or not is an intricate problem. How can we test and quantify the goodness of a certain distribution to the data?” (Tests)
“It takes months to estimate by simulation the probability of a very rare event, e.g., a bit error rate of 10-10. Is there any other way around this?” (Discrete Event Simulation)
Answers to all the aforementioned questions and their implications will be taught in the course.