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Adaptive Resource Management (ARM)

Lupe [1]

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... [2]

Team

Lupe [3]

Lauritz [4], Morgan [5], Ilja [6], Kordian [7], Dominik [8], Philipp [9], Jonathan B. [10], and Jonathan W. [11]

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:

  • Resource Allocation for Distributed Processing [12]
  • Scheduling of Data Processing Jobs [13]
  • Auto-Tuning of Scalable Systems [14]
  • Dependable IoT Systems [15]

Recent Publications

next >> [32]

Will, Jonathan and Arslan, Onur and Bader, Jonathan and Scheinert, Dominik and Thamsen, Lauritz (2021). Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud [36]. International Conference on Big Data. IEEE, to appear.


Tilcher, David Konstantin and Popescu, Florin and Sommer, Harald and Thamsen, Lauritz and Thamsen, Paul Uwe (2021). Control Optimization Through Prediction-Based Wastewater Management [37]. Proceedings of the ASME 2021 Fluids Engineering Division Summer Meeting. ASME, 9.


Scheinert, Dominik and Thamsen, Lauritz and Zhu, Houkun and Will, Jonathan and Acker, Alexander and Wittkopp, Thorsten and Kao, Odej (2021). Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts [38]. 2021 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 261-270.


next >> [55]

Contact

Lauritz Thamsen
+49 30 314-24539
TEL
Room 1210
e-mail query [56]
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