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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

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.


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.


Thamsen, Lauritz and Verbitskiy, Ilya and Beilharz, Jossekin and Renner, Thomas and Polze, Andreas and Kao, Odej (2017). Ellis: Dynamically Scaling Distributed Dataflows to Meet Runtime Targets. Proceedings of the 2017 IEEE 9th International Conference on Cloud Computing Technology and Science. IEEE, 146–153.


Zusatzinformationen / Extras

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Contact

Lauritz Thamsen
+49 30 314-24539
TEL
Room 1210