Projects

MAPLE is a collaborative research project between our chair and Snowflake, Inc., focusing on advancing performance benchmarking methodologies for cloud data applications, enabling better infrastructure evaluation and optimization decisions.

Our research group develops a framework for serverless scientific computing to make large-scale Earth observation (EO) workflows more reusable, reproducible, and accessible. By combining computer science, geosciences, and ecology, we aim to automate and simplify the processing of petabyte-scale, heterogeneous satellite and environmental data—removing technical barriers for researchers. This interdisciplinary approach opens new opportunities to study pressing sustainability questions, from climate impacts on snow cover to biodiversity shifts driven by land surface dynamics.

MicroGenerator is a novel framework designed to unlock the potential of transfer learning for microservice architectures. By automating the creation and benchmarking of synthetic microservice applications using a library of reusable building blocks, it addresses the lack of large training datasets—a key barrier to applying transfer learning in areas like autoscaling, energy-efficient placement, and resource saturation detection.

The Minerva project focuses on predicting both transient and steady-state performance of microservice applications in container orchestration environments, such as those managed by Kubernetes. By developing new models, simulation algorithms, and benchmarks, as well as treating adaptation mechanisms of container orchestration frameworks as black-boxes, it aims to provide comprehensive and tangible insights into system behaviors and optimization opportunities.

Marie Skłodowska-Curie Actions Staff Exchange Project founded by the European Union

Interdisciplinary Cooperation Project (since 10/2021)

Cooperation Project with DLR (since 2020)