Institutsseminar/2020-07-03

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Termin (Alle Termine)
Datum Fr 3. Juli 2020, 11:30 Uhr
Dauer 90 min
Raum https://sdqweb.ipd.kit.edu/wiki/Institutsseminar/Microsoft_Teams
Vorheriger Termin Fr 26. Juni 2020
Nächster Termin Fr 10. Juli 2020

Vorträge

Vortragende(r) Frederick Persch
Titel Evaluation architekturbasierter Performance-Vorhersage im Kontext automatisierter Fahrzeuge
Vortragstyp Masterarbeit
Betreuer(in) Sebastian Krach
Kurzfassung In the past decades, there has been an increased interest in the development of automated vehicles. Automated vehicles are vehicles that are able to drive without the need for constant interaction by a human driver. Instead they use multiple sensors to observe their environment and act accordingly to observed stimuli. In order to avoid accidents, the reaction to these stimuli needs to happen in a sufficiently short amount of time. To keep implementation overhead and cost low, it is highly beneficial to know the reaction time of a system as soon as possible. Thus, being able to assess their performance already at design time allows system architects to make informed decisions when comparing software components for the use in automated vehicles. In the presented thesis, I analysed the applicability of architecture-based performance prediction in the context of automated vehicles using the Palladio Approach. In particular, I focused on the prediction of design-time worst-case reaction time as the reaction ability of automated vehicles, which is a crucial metric when assessing their performance.
Vortragende(r) Alexis Bernhard
Titel Pattern Matching for Microservices in a Container-Based Architecture
Vortragstyp Masterarbeit
Betreuer(in) Yves Schneider
Kurzfassung Multiple containers as packages of software code can interact with each other in a network and build together a container-based architecture. Huge architectures are hard to understand without any knowledge about the application or the applied underlying technologies. Therefore, this master thesis uses the approach of design pattern detection to reduce the amount of complexity of one architecture representation to multiple smaller pattern instances. So, a user can understand the depicted pattern instances in a short period of time by knowing the general patterns in advance.
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Hinweise

Das Sekretariat hat überprüft, dass Prof. Reussner anwesend ist, allerdings kann Prof. Reussner nur am Vormittag anwesend sein.