| Integration of module into courses of studies: |
Informatik, M.Sc., FSPO 2021/Kernfach/Technische und Systemnahe Informatik
Informatik, M.Sc., FSPO 2021/Vertiefungsfach/Verteilte Systeme
Künstliche Intelligenz, M.Sc., FSPO 2021/Kernfach Künstliche Intelligenz/Technische und Systemnahe Informatik
Medieninformatik, M.Sc., FSPO 2021/Kernfach/Technische und Systemnahe Informatik
Medieninformatik, M.Sc., FSPO 2021/Vertiefungsfach Medieninformatik/Verteilte Systeme
Software Engineering, M.Sc., FSPO 2021/Kernfach/Technische und Systemnahe Informatik
Software Engineering, M.Sc., FSPO 2021/Vertiefungsfach Software Engineering/Verteilte und Eingebettete Systeme
Informatik, B.Sc., FSPO 2022/Vertiefungsbereich
Informatik, M.Sc., FSPO 2022/Kernbereich Informatik/Technische Informatik
Künstliche Intelligenz, M.Sc., FSPO 2022/Kernbereich Künstliche Intelligenz/Technische Informatik
Medieninformatik, B.Sc., FSPO 2022/Vertiefungsbereich
Medieninformatik, M.Sc., FSPO 2022/Kernbereich Medieninformatik/Technische Informatik
Software Engineering, B.Sc., FSPO 2022/Vertiefungsbereich/SE Wahlbereich
Software Engineering, M.Sc., FSPO 2022/Kernbereich Software Engineering/Technische Informatik |
| Modes of learning and teaching: |
|
| Module authority: |
Prof. Dr. Frank Kargl |
| Lecturer: |
Dr. Benjamin Erb, Dr. Jörg Domaschka |
| Language: |
English/Deutsch |
| Turn / Duration: |
Every summer term / one semester |
| Requirements (contentual): |
Fundamentals of operating systems, computer networks and distributed systems |
| Requirements (formal): |
– |
| Basis for: |
– |
| Learning objectives: |
Knowledge and Understanding:
Basic Statistics: Understanding the foundations for explorative and hypothesis-testing data analyses by applying descriptive statistics and statistical inference
System Characteristics: Knowing relevant properties for computer systems under test in terms of varying execution environments (software/hardware)
Performance Metrics and Indicators: Understanding core performance metrics (e.g., latency, throughput, resource utilization) and how to diagnose and interpret them
Benchmarking Techniques: Comprehending relevant evaluation and benchmarking methodologies and their applicability for different types of computer systems
Performance Engineering Principles: Understanding the foundational principles of performance engineering for different systems and at different system levels and explain possible optimization paths
Skills and Abilities:
select appropriate concepts and methodologies for evaluating the performance of different computer systems
design, execute, and report own performance evaluations for different types of systems
document and report the results of own performance evaluation, including the use of appropriate data visualizations
critically assess and challenge existing performance evaluations and identify potential problems and deficiencies
apply performance engineering techniques systematically |
| Content: |
This course explores the fundamental principles, concepts, and methodologies for evaluating, engineering, and diagnosing the performance of computer systems.
After covering the relevant statistical fundamentals, the course introduces key performance metrics, core measurement concepts, and established benchmarking techniques.
Here, the course considers different types of computer systems and illustrates specific evaluation challenges as well as potential performance engineerings steps for these systems. Covered system types include applications, network-based services, distributed applications, database management systems, and cloud-based and containerized systems.
In addition to the theoretical aspects introduced in the lecture, the labs will provide hands-on experience in testing and measuring performance characteristics of various types of systems. |
| Literature: |
Lecture slides and selected literature referenced in the lecture. |
| Grading procedure: |
The module examination consists of a graded written or oral examination, depending on the number of participants. If a specified academic work is achieved, a grade bonus is awarded in accordance with §17 (3a) of the General Examination Regulations at the immediately following examination. The examination grade is improved by one grade level, but not better than 1.0. An improvement from 5.0 to 4.0 is not possible. The examination form will be announced in good time before the examination is held - at least 4 weeks before the examination date. |
| Estimation of effort: |
Active time: 60 h
Preparation and evaluation: 120 h
Sum: 180 h |
