rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Long short-term memory recurrent neural networks models to forecast the resource usage of MapReduce applications
  name:
    name part: Li Yangyuan
    role:
      role term: creator
  name:
    name part: Do Tien Van
    role:
      role term: aut
  name:
    name part: Conference of PhD students in computer science (11.) (2018) (Szeged)
  type of resource: text
  genre: conference publication
  origin info:
    place:
      place term: hu
    date issued: 2018
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: elektronikus dokumentum
    extent: 176-178
  abstract: The forecasting of the resource usage of MapReduce applications plays an important role in the operation of cloud infrastructure. In this paper, we apply long short-term memory recurrent neural networks to predict the resource usage of three representative MapReduce applications. The Results show that the Long Short-term Memory Recurrent Neural Networks models perform higher prediction accuracy than persistence ones. Predictions of other usage parameters show similar accuracy with persistence one. The improper configuration parameters of Long Short-term Memory Recurrent Neural Networks possibly result in few of worse prediction.
  note: Li Yangyuan
  related item:
    title info:
      title: Conference of PhD Students in Computer Science 11
  identifier: http://acta.bibl.u-szeged.hu/61797/1/cscs_2018_189-191.pdf
  record info:
    record content source: SZTE Egyetemi Kiadványok Repozitórium
    record creation date: 191104
    record change date: 20260224081030.0
    record identifier: acta61797
    language of cataloging:
      language term: hun