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@INPROCEEDINGS{Rybicki:862006,
      author       = {Rybicki, Jedrzej},
      title        = {{T}owards {P}redictive {M}onitoring of {R}esearch
                      {I}nfrastructures},
      publisher    = {IARIA},
      reportid     = {FZJ-2019-02380},
      isbn         = {978-1-61208-700-9},
      pages        = {37-40},
      year         = {2019},
      comment      = {ALLDATA 2019, The Fifth International Conference on Big
                      Data, Small Data, Linked Data and Open Data},
      booktitle     = {ALLDATA 2019, The Fifth International
                       Conference on Big Data, Small Data,
                       Linked Data and Open Data},
      abstract     = {Nowadays, both modern computing infrastructures, as well as
                      their scientific workloads exhibit far reaching complexity
                      and diversity. Therefore, it is increasingly hard to
                      comprehend and manage them in an efficient manner. In
                      particular, it can lead to under- or overuse of resources. A
                      prerequisite for efficient resource allocation is the
                      ability to predict their usage. In this paper, we use real
                      world workloads recorded in a modern research infrastructure
                      to conduct load prediction. We use well established
                      statistical model (ARIMA) and achieve good results in
                      predictions. The big data challenge is here given not by the
                      sheer size but rather by the speed of data collection and
                      processing. The quicker the prediction is made, the more
                      time is available for actions. Such predictions can be
                      included in monitoring systems to give human operators
                      better insights into the status of their infrastructures and
                      lead to better load distribution.},
      month         = {Mar},
      date          = {2019-03-24},
      organization  = {The Fifth International Conference on
                       Big Data, Small Data, Linked Data and
                       Open Data, Valencia (Spain), 24 Mar
                       2019 - 28 Mar 2019},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512)},
      pid          = {G:(DE-HGF)POF3-512},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/862006},
}