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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

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next >> [33]

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 [37]. 2021 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 261-270.


Scheinert, Dominik and Zhu, Houkun and Thamsen, Lauritz and Geldenhuys, Morgan K. and Will, Jonathan and Acker, Alexander and Kao, Odej (2021). Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation [38]. 40th IEEE International Performance Computing and Communications Conference. IEEE, to appear.


Scheinert, Dominik and Alamgiralem, Alireza and Bader, Jonathan and Will, Jonathan and Wittkopp, Thorsten and Thamsen, Lauritz (2021). On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds [39]. 2021 IEEE International Conference on Big Data. IEEE, to appear.


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Contact

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