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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]


Lupe [3]

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


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

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Lorenz, Felix and Geldenhuys, Morgan and Sommer, Harald and Jakobs, Frauke and L├╝ring, Carsten and Skwarek, Volker and Behnke, Ilja and Thamsen, Lauritz (2020). A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring [37]. 2020 IEEE International Conference on Big Data. IEEE, 3488–3493.

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 [38]. 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 [39]. In the Proceedings of the 6th 2017 IEEE International Congress on Big Data (BigData Congress 2017). IEEE, 145–152.

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Lauritz Thamsen
+49 30 314-24539
Room 1210
e-mail query [58]
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