TU Berlin

Department of Telecommunication SystemsAdaptive Resource Management

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Adaptive Resource Management (ARM)


One of the areas we work on in the DOS group is adaptive resource management for data-intensive distributed applications, aiming at systems that automatically adapt to workloads and environments. Learn more...




Aiming to make it easier to run data-intensive applications efficiently on distributed computing infrastructures from small devices to large-scale clusters, we work on adaptive resource management, following an iterative systems research approach.

Current research directions include:

Recent Publications

Thomas Renner and Lauritz Thamsen and Odej Kao (2017). Adaptive Resource Management for Distributed Data Analytics based on Container-level Cluster Monitoring. Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,. SciTePress, 38–47.

Renner, Thomas and Müller, Johannes and Thamsen, Lauritz and Kao, Odej (2017). Addressing Hadoop's Small File Problem With an Appendable Archive File Format. Proceedings of the Computing Frontiers Conference. ACM, 367–372.

Scheinert, Dominik and Thamsen, Lauritz and Zhu, Houkun and Will, Jonathan and Acker, Alexander and Wittkopp, Thorsten and Kao, Odej (2021). Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts. 2021 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 261-270.


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