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| Detailinformationen |
| Quellcurriculum |
Masterstudium Computer Science 2025W |
| Lernergebnisse |
Kompetenzen |
| (*)Students have acquired both theoretical and practical skills to effectively model, measure, analyze, and predict the performance of web systems.
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| Fertigkeiten |
Kenntnisse |
(*)Students
- understand key performance metrics (K2), such as response time, throughput, utilisation as well as web specific metrics, such as Time to First Byte (TTFB), First Contentful Paint (FCP), Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), First Input Delay (FID), etc.
- master basic performance modelling techniques, such as operational analysis, queuing network models, stochasatic modelling, performance bounds and being able to apply them to selected performance evaluation problems (K3)
- are skilled in using tools for measuring web performance in real world scenarios (K3)
- are able to interpret performance evaluation results (K4)
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(*)- Basic concepts regarding key performance data and models
- Methods in performance evaluation (modeling and measuring)
- Analysis tools
- Case studies
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| Beurteilungskriterien |
(*)attendance and participation, mini project
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| Lehrmethoden |
(*)lecture, case studies, homework
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| Abhaltungssprache |
Englisch |
| Lehrinhalte wechselnd? |
Nein |
| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
921CGELCAPK13: KV Capacity Planning (2013W-2019S)
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