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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

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.

Ulf Leser, Odej Kao, and others (2021). The Collaborative Research Center FONDA. Datenbank Spektrum. Springer.

Thamsen, Lauritz and Scheinert, Dominik and Will, Jonathan and Bader, Jonathan and Kao, Odej (2022). Collaborative Cluster Configuration for Distributed Data-Parallel Processing: A Research Overview. Datenbank-Spektrum. Springer.

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Lauritz Thamsen
+49 30 314-24539