@article{ThamsenBeilharzVinhThuyTranetal.2020,
  author    = {Thamsen, Lauritz and Beilharz, Jossekin Jakob and Vinh Thuy Tran, and Nedelkoski, Sasho and Kao, Odej},
  title     = {Mary, Hugo, and Hugo*},
  series = {Concurrency and computation : practice \& experience},
  volume    = {33},
  journal   = {Concurrency and computation : practice \& experience},
  number    = {18},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1532-0626},
  doi       = {10.1002/cpe.5823},
  pages     = {12},
  year      = {2020},
  abstract  = {Distributed data-parallel processing systems like MapReduce, Spark, and Flink are popular for analyzing large datasets using cluster resources. Resource management systems like YARN or Mesos in turn allow multiple data-parallel processing jobs to share cluster resources in temporary containers. Often, the containers do not isolate resource usage to achieve high degrees of overall resource utilization despite overprovisioning and the often fluctuating utilization of specific jobs. However, some combinations of jobs utilize resources better and interfere less with each other when running on the same shared nodes than others. This article presents an approach for improving the resource utilization and job throughput when scheduling recurring distributed data-parallel processing jobs in shared clusters. The approach is based on reinforcement learning and a measure of co-location goodness to have cluster schedulers learn over time which jobs are best executed together on shared resources. We evaluated this approach over the last years with three prototype schedulers that build on each other: Mary, Hugo, and Hugo*. For the evaluation we used exemplary Flink and Spark jobs from different application domains and clusters of commodity nodes managed by YARN. The results of these experiments show that our approach can increase resource utilization and job throughput significantly.},
  language  = {en}
}
@article{SteinertThamsenFelgentreffetal.2015,
  author    = {Steinert, Bastian and Thamsen, Lauritz and Felgentreff, Tim and Hirschfeld, Robert},
  title     = {Object Versioning to Support Recovery Needs Using Proxies to Preserve Previous Development States in Lively},
  series = {ACM SIGPLAN notices},
  volume    = {50},
  journal   = {ACM SIGPLAN notices},
  number    = {2},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  issn      = {0362-1340},
  doi       = {10.1145/2661088.2661093},
  pages     = {113 -- 124},
  year      = {2015},
  abstract  = {We present object versioning as a generic approach to preserve access to previous development and application states. Version-aware references can manage the modifications made to the target object and record versions as desired. Such references can be provided without modifications to the virtual machine. We used proxies to implement the proposed concepts and demonstrate the Lively Kernel running on top of this object versioning layer. This enables Lively users to undo the effects of direct manipulation and other programming actions.},
  language  = {en}
}
@book{OttoPollakWerneretal.2015,
  author    = {Otto, Philipp and Pollak, Jaqueline and Werner, Daniel and Wolff, Felix and Steinert, Bastian and Thamsen, Lauritz and Taeumel, Marcel and Lincke, Jens and Krahn, Robert and Ingalls, Daniel H. H. and Hirschfeld, Robert},
  title     = {Exploratives Erstellen von interaktiven Inhalten in einer dynamischen Umgebung​},
  number    = {101},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-346-6},
  issn      = {1613-5652},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-83806},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {vii, 115},
  year      = {2015},
  abstract  = {Bei der Erstellung von Visualisierungen gibt es im Wesentlichen zwei Ans{\"a}tze. Zum einen k{\"o}nnen mit geringem Aufwand schnell Standarddiagramme erstellt werden. Zum anderen gibt es die M{\"o}glichkeit, individuelle und interaktive Visualisierungen zu programmieren. Dies ist jedoch mit einem deutlich h{\"o}heren Aufwand verbunden. Flower erm{\"o}glicht eine schnelle Erstellung individueller und interaktiver Visualisierungen, indem es den Entwicklungssprozess stark vereinfacht und die Nutzer bei den einzelnen Aktivit{\"a}ten wie dem Import und der Aufbereitung von Daten, deren Abbildung auf visuelle Elemente sowie der Integration von Interaktivit{\"a}t direkt unterst{\"u}tzt.},
  language  = {de}
}