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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

Geldenhuys, Morgan K. and Thamsen, Lauritz and Gontarska, Kain Kordian and Lorenz, Felix and Kao, Odej (2019). Effectively Testing System Configurations of Critical IoT Analytics Pipelines. 2019 IEEE International Conference on Big Data. IEEE, 4157–4162.


Thamsen, Lauritz and Verbitskiy, Ilya and Nedelkoski, Sasho and Tran, Vinh Thuy and Meyer, Vinicius and Xavier, Miguel G. and Kao, Odej and De Rose, Cesar A. F. (2019). . Euro-Par 2019: Parallel Processing Workshops. Springer, 519–530.


Verbitskiy, Ilya and Thamsen, Lauritz and Renner, Thomas and Kao, Odej (2018). CoBell: Runtime Prediction for Distributed Dataflow Jobs in Shared Clusters. 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, 81–88.


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Contact

Lauritz Thamsen
+49 30 314-24539
TEL
Room 1210