Balanced graph partitioning with apache spark (Contributo in volume (capitolo o saggio))

Type
Label
  • Balanced graph partitioning with apache spark (Contributo in volume (capitolo o saggio)) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-319-14325-5_12 (literal)
Alternative label
  • Carlini E., Dazzi P., Esposito A., Lulli A., Ricci L. (2014)
    Balanced graph partitioning with apache spark
    in , 2014
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Carlini E., Dazzi P., Esposito A., Lulli A., Ricci L. (literal)
Pagina inizio
  • 129 (literal)
Pagina fine
  • 140 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.springer.com/chapter/10.1007%2F978-3-319-14325-5_12 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 8805 (literal)
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Itay; University of Pisa, Italy; University of Pisa, Italy; University of Pisa, Italy (literal)
Titolo
  • Balanced graph partitioning with apache spark (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 9783319143248 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Luís Lopes, Julius ?ilinskas, Alexandru Costan (et al...) (literal)
Abstract
  • A significant part of the data produced every day by online services is structured as a graph. Therefore, there is the need for efficient processing and analysis solutions for large scale graphs. Among the others, the balanced graph partitioning is a well known NP-complete problem with a wide range of applications. Several solutions have been proposed so far, however most of the existing state-of-the-art algorithms are not directly applicable in very large-scale distributed scenarios. A recently proposed promising alternative exploits a vertex-center heuristics to solve the balance graph partitioning problem. Their algorithm is massively parallel: there is no central coordination, and each node is processed independently. Unfortunately, we found such algorithm to be not directly exploitable in current BSP-like distributed programming frameworks. In this paper we present the adaptations we applied to the original algorithm while implementing it on Spark, a state-of-the-art distributed framework for data processing. (literal)
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