Institutsseminar/2020-07-03

Termin (Alle Termine)

	Datum	Freitag, 3. Juli 2020
	Uhrzeit	11:30 – 13:00 Uhr (Dauer: 90 min)
	Ort	https://sdqweb.ipd.kit.edu/wiki/Institutsseminar/Microsoft_Teams
	Webkonferenz
	Vorheriger Termin	Fr 26. Juni 2020
	Nächster Termin	Fr 10. Juli 2020

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Vorträge

Vortragende(r)	Frederick Persch
Titel	Evaluation architekturbasierter Performance-Vorhersage im Kontext automatisierter Fahrzeuge
Vortragstyp	Masterarbeit
Betreuer(in)	Sebastian Krach
Vortragssprache
Vortragsmodus
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.

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Vortragende(r)	Alexis Bernhard
Titel	Pattern Matching for Microservices in a Container-Based Architecture
Vortragstyp	Masterarbeit
Betreuer(in)	Yves Kirschner
Vortragssprache
Vortragsmodus
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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