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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

Janßen, Gerrit and Verbitskiy, Ilya and Renner, Thomas and Thamsen, Lauritz (2018). Scheduling Stream Processing Tasks on Geo-Distributed Heterogeneous Resources. 2018 IEEE International Conference on Big Data. IEEE, 5159–5164.


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.


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.


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Contact

Lauritz Thamsen
+49 30 314-24539
TEL
Room 1210