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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

<< previous [16]
next >> [33]

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 [37]. 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 [38]. 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 [39]. 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
e-mail query [58]
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