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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

Siling Chen, Florian Brokhausen, Philipp Wiesner, Lauritz Thamsen, and Andrea Cominola (2021). Assessing the Resilience of Water Distribution Networks Under Different Sensor Network Architectures and Data Sampling Frequencies. Proceedings of the 2nd International Symposium on Water System Operations (ISWSO). IAHR, 1-2.


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.


Scheinert, Dominik and Zhu, Houkun and Thamsen, Lauritz and Geldenhuys, Morgan K. and Will, Jonathan and Acker, Alexander and Kao, Odej (2021). Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation. 40th IEEE International Performance Computing and Communications Conference. IEEE, to appear.


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Contact

Lauritz Thamsen
+49 30 314-24539
TEL
Room 1210