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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

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.


Lauritz Thamsen, Jossekin Beilharz, Vinh Thuy Tran, Sasho Nedelkoski, and Odej Kao (2020). Mary, Hugo, and Hugo*: Learning to Schedule Distributed Data-Parallel Processing Jobs on Shared Clusters. Concurrency and Computation: Practice and Experience. Wiley, e5823.


Lauritz Thamsen, Benjamin Rabier, Florian Schmidt, Thomas Renner, and Odej Kao (2017). Scheduling Recurring Distributed Dataflow Jobs Based on Resource Utilization and Interference. In the Proceedings of the 6th 2017 IEEE International Congress on Big Data (BigData Congress 2017). IEEE, 145–152.


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Contact

Lauritz Thamsen
+49 30 314-24539
TEL
Room 1210