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Adaptive Resource Management (ARM)

Lupe
© ARM / DOS / TUB

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

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Scheinert, Dominik and Alamgiralem, Alireza and Bader, Jonathan and Will, Jonathan and Wittkopp, Thorsten and Thamsen, Lauritz (2021). On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds. 2021 IEEE International Conference on Big Data. IEEE, to appear.

Bader, Jonathan and Thamsen, Lauritz and Kulagina, Svetlana and Will, Jonathan and Meyerhenke, Henning and Kao, Odej (2021). Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters. 2021 IEEE International Conference on Big Data. IEEE, to appear.

Bermbach, David and Bader, Jonathan and Hasenburg, Jonathan and Pfandzelter, Tobias and Thamsen, Lauritz (2021). AuctionWhisk: Using an Auction-Inspired Approach for Function Placement in Serverless Fog Platforms. Software: Practice and Experience. Wiley, to appear.

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Contact

Lauritz Thamsen
 +49 30 314-24539
TEL
Room 1210