Design-time optimization of runtime adaptation strategies using Reinforcement learning-based methods

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Typ Masterarbeit
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Martina Rapp (E-Mail:, Telefon: +49-721-9654-645)


Self-adaptive software systems (SAS) are an important class of software applications. Examples are IoT systems and IaaS/cloud systems like scalable web services. Since Reinfocment learning (RL) and Machine learning (ML) methods have developed significantly over the past few years, we would like to try the following:


The thesis aims to explore the idea of applying Reinforcement learning-based methods for the design-time optimization of runtime adaptation strategies.

The main tasks will be:

  • Conceptualize the idea
  • Formalize the idea: elaborate the theoretical foundation to apply RL methods
  • Implement and evaluate the concept


  • Insight into a highly relevant field of research
  • Opportunity to develop innovative technologies
  • Opportunity to contribute to a scientific publication
  • Very good working environment and intensive support