TU Berlin

Department of Telecommunication SystemsAdaptive Resource Management

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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

Thamsen, Lauritz and Verbitskiy, Ilya and Rabier, Benjamin and Kao, Odej (2018). Learning Efficient Co-locations for Scheduling Distributed Dataflows in Shared Clusters. Services Transactions on Big Data. Services Society, 1–15.


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.


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.


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