MA: Adaptive Task-Resource Matching for Large-Scale Scientific Workflows
This topic area includes a larger set of different thesis topics
- Bachelor/Master: Approaches to describe heterogeneous hardware infrastructures
- Bachelor/Master: Usage of reinforcement learning to match tasks to heterogeneous infrastructure elements
- Bachelor/Master: Task profiling on heterogeneous infrastructures
- Bachelor/Master: Scheduling and task-resource matching of large-scale scientific workflows
All topics evaluate their approaches with scientific workflow frameworks and example workflows.
Prerequisites for working on this topic are advanced knowledge in Docker and excellent programming skills in at least one programming language like Java, Scala, Groovy, Kotlin, or Python.
Students interested in topics not mentioned above but which relate to scheduling, profiling, infrastructure descriptions, or scientific workflows are welcome to send an email.
Thesis can be written in either German or English language.