TU Berlin

Department of Telecommunication SystemsAdaptive Resource Management

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Adaptive Resource Management (ARM)


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...




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

Koch, Jannis and Thamsen, Lauritz and Schmidt, Florian and Kao, Odej (2017). SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows. The 18th International Conference on Parallel and Distributed Computing, Applications and Technologies. IEEE, 156–163.

Thamsen, Lauritz and Renner, Thomas and Verbitskiy, Ilya and Kao, Odej (2018). Adaptive Resource Management for Distributed Data Analytics. Advances in Parallel Computing – Big Data and HPC: Ecosystem and Convergence. IOS Press, 155–170.

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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