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Adaptive Resource Management (ARM)

Lupe [1]
© 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... [2]

Team

Lupe [3]
© DOS / TUB

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

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 [11]
  • Scheduling of Data Processing Jobs [12]
  • Auto-Tuning of Scalable Systems [13]
  • Dependable IoT Systems [14]

Recent Publications

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Order by: Author [34]  Year [35]  Journal [36]

Will, Jonathan and Thamsen, Lauritz and Bader, Jonathan and Scheinert, Dominik and Kao, Odej (2022). Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale Data Processing. IEEE International Conference on Cloud Engineering (IC2E). IEEE, to appear.

Zhu, Houkun and Scheinert, Dominik and Thamsen, Lauritz and Gontarska, Kordian and Kao, Odej (2022). Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning. IEEE International Conference on Cloud Engineering (IC2E). IEEE, to appear.

Wiesner, Philipp and Scheinert, Dominik and Wittkopp, Thorsten and Thamsen, Lauritz and Kao, Odej (2022). Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads [37]. 28th International European Conference on Parallel and Distributed Computing (Euro-Par). Springer, to appear.

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Contact

Lauritz Thamsen
 +49 30 314-24539
TEL
Room 1210
e-mail query [57]
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