Forschungsthemen
[MA] Adaptive Knowledge Exchange with Distributed Partial Models@run.time
Future software systems will be highly dynamic. We are already experiencing, for example, a world where Cyber-Physical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements; they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization. The focus of the Master thesis is on the problem of knowledge sharing among cooperating entities of a highly distributed and self-adaptive CPS. Specifically, the research question to address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., models reflecting only part of the state of the CPS, is to be investigated. The approach shall support two adaptation dimensions: the runtime type of knowledge and conditions over the knowledge. The following tasks have to be accomplished in this master thesis:
- A set of requirements for the above described approached have to be identified
- Related work has to be identified and classified according to the requirements
- A conceptual framework for adaptive knowledge exchange with distributed, partial runtime models
- A prototypical implementation of this framework
- An evaluation based on either a cleaning robot or other scenario
Betreuer: Sebastian Götz