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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...

Team

Lupe

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

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.


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