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Adaptive Resource Management (ARM)

Lupe

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...

Research

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

Thamsen, Lauritz and Verbitskiy, Ilya and Schmidt, Florian and Renner, Thomas and Kao, Odej (2016). Selecting Resources for Distributed Dataflow Systems According to Runtime Targets. In the Proceedings of the 35th IEEE International Performance Computing and Communications Conference (IPCCC). IEEE, 1–8.


Thamsen, Lauritz and Renner, Thomas and Byfeld, Marvin and Paeschke, Markus and Schröder, Daniel and Böhm, Felix (2016). Visually Programming Dataflows for Distributed Data Analytics. In the Proceedings of the 2016 IEEE International Conference on Big Data (BigData). IEEE, 2276–2285.


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.


Zusatzinformationen / Extras

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Contact

Lauritz Thamsen
+49 30 314-24539
TEL
Room 1210