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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

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.


Wiesner, Philipp and Behnke, Ilja and Scheinert, Dominik and Gontarska, Kordian and Thamsen, Lauritz (2021). Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud. 22nd International Middleware Conference (Middleware). ACM.


Wiesner, Philipp and Scheinert, Dominik and Wittkopp, Thorsten and Thamsen, Lauritz and Kao, Odej (2022). Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads. 28th International European Conference on Parallel and Distributed Computing (Euro-Par). Springer, to appear.


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Contact

Lauritz Thamsen
+49 30 314-24539
TEL
1210