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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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next >> [33]

Felgentreff, Tim and Lincke, Jens and Hirschfeld, Robert and Thamsen, Lauritz (2015). Lively Groups: Shared Behavior in a World of Objects without Classes or Prototypes [37]. In Proceedings of the Future Programming Workshop (FPW) 2015, co-located with the Conference on Object-oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM, 15–22.

Renner, Thomas and Thamsen, Lauritz and Kao, Odej (2015). Network-Aware Resource Management for Scalable Data Analytics Frameworks [38]. Proceedings of the 1st First Workshop on Data-Centric Infrastructure for Big Data Science (DIBS), co-located with the 2015 IEEE International Conference on BigData. IEEE, 2793–2800.

Thamsen, Lauritz and Steinert, Bastian and Hirschfeld, Robert (2015). Preserving Access to Previous System States in the Lively Kernel [39]. Design Thinking Research. Springer, 235–264.

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