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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

Lorenz, Felix and Thamsen, Lauritz and Wilke, Andreas and Behnke, Ilja and Waldmüller-Littke, Jens and Komarov, Ilya and Kao, Odej and Paeschke, Manfred (2020). Fingerprinting Analog IoT Sensors for Secret-Free Authentication. 2020 29th International Conference on Computer Communications and Networks (ICCCN). IEEE, 1–6.


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.


Behnke, Ilja and Thamsen, Lauritz and Kao, Odej (2019). Héctor: A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds. 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion. ACM, 15–20.


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Contact

Lauritz Thamsen
+49 30 314-24539
TEL
Room 1210