Institutsseminar/2021-07-23: Unterschied zwischen den Versionen
|Zeile 1:||Zeile 1:|
Aktuelle Version vom 14. Januar 2022, 14:15 Uhr
|Datum||Fr 23. Juli 2021, 14:00 Uhr|
|Vorheriger Termin||Fr 23. Juli 2021|
|Nächster Termin||Fr 30. Juli 2021|
|Titel||Architectural Uncertainty Analysis for Access Control Scenarios in Industry 4.0|
|Kurzfassung||In this thesis, we present our approach to handle uncertainty in access control during design time. We propose the concept of trust as a composition of environmental factors that impact the validity of and consequently trust in access control properties. We use fuzzy inference systems as a way of defining how environmental factors are combined. These trust values are than used by an analysis process to identify issues which can result from a lack of trust.
We extend an existing data flow diagram approach with our concept of trust. Our approach of adding knowledge to a software architecture model and providing a way to analyze model instances for access control violations shall enable software architects to increase the quality of models and further verify access control requirements under uncertainty. We evaluate the applicability based on the availability, the accuracy and the scalability regarding the execution time.
|Titel||Evaluating architecture-based performance prediction for MPI-based systems|
|Kurzfassung||One research field of High Performance Computing (HPC) is computing clusters. Computing clusters are distributed memory systems where different machines are connected through a network. To enable the machines to communicate with each other they need the ability to pass messages to each other through the network. The Message Passing Interface (MPI) is the standard in implementing parallel systems for distributed memory systems. To enable software architects in predicting the performance of MPI-based systems several approaches have been proposed. However, those approaches depend either on an existing implementation of a program or are tailored for specific programming languages or use cases. In our approach, we use the Palladio Component Model (PCM) that allows us to model component-based architectures and to predict the performance of the modeled system. We modeled different MPI functions in the PCM that serve as reusable patterns and a communicator that is required for the MPI functions. The expected benefit is to provide patterns for different MPI functions that allow a precise modelation of MPI-based systems in the PCM. And to obtain a precise performance prediction of a PCM instance.|
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