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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

Lauritz Thamsen, Benjamin Rabier, Florian Schmidt, Thomas Renner, and Odej Kao (2017). Scheduling Recurring Distributed Dataflow Jobs Based on Resource Utilization and Interference. In the Proceedings of the 6th 2017 IEEE International Congress on Big Data (BigData Congress 2017). IEEE, 145–152.

Koch, Jannis and Thamsen, Lauritz and Schmidt, Florian and Kao, Odej (2017). SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows. The 18th International Conference on Parallel and Distributed Computing, Applications and Technologies. IEEE, 156–163.

Thamsen, Lauritz and Renner, Thomas and Verbitskiy, Ilya and Kao, Odej (2018). Adaptive Resource Management for Distributed Data Analytics. Advances in Parallel Computing – Big Data and HPC: Ecosystem and Convergence. IOS Press, 155–170.

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Lauritz Thamsen
+49 30 314-24539