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Description
  • Stream processing is a paradigm evolving in response to well-known limitations of widely adopted MapReduce paradigm for big data processing, a hot topic of today's computer world. Moreover, in the field of computation facilities, heterogeneity of data processing clusters, intended or unintended, is starting to be relatively common. This paper deals with scheduling problems and decisions in stream processing on heterogeneous clusters. It brings an overview of current state of the art of stream processing on heterogeneous clusters with focus on resource allocation and scheduling. Basic scheduling decisions are discussed and demonstrated on naive scheduling of a sample application. The paper presents a proposal of a novel scheduler for stream processing frameworks on heterogeneous clusters, which employs design-time knowledge as well as benchmarking techniques to achieve optimal resource-aware deployment of applications over the clusters and eventually better overall utilization of the cluste
  • Stream processing is a paradigm evolving in response to well-known limitations of widely adopted MapReduce paradigm for big data processing, a hot topic of today's computer world. Moreover, in the field of computation facilities, heterogeneity of data processing clusters, intended or unintended, is starting to be relatively common. This paper deals with scheduling problems and decisions in stream processing on heterogeneous clusters. It brings an overview of current state of the art of stream processing on heterogeneous clusters with focus on resource allocation and scheduling. Basic scheduling decisions are discussed and demonstrated on naive scheduling of a sample application. The paper presents a proposal of a novel scheduler for stream processing frameworks on heterogeneous clusters, which employs design-time knowledge as well as benchmarking techniques to achieve optimal resource-aware deployment of applications over the clusters and eventually better overall utilization of the cluste (en)
Title
  • Scheduling Decisions in Stream Processing on Heterogeneous Clusters
  • Scheduling Decisions in Stream Processing on Heterogeneous Clusters (en)
skos:prefLabel
  • Scheduling Decisions in Stream Processing on Heterogeneous Clusters
  • Scheduling Decisions in Stream Processing on Heterogeneous Clusters (en)
skos:notation
  • RIV/00216305:26230/14:PU111957!RIV15-MSM-26230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
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  • 43879
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26230/14:PU111957
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • scheduling, resource-awareness, benchmarking, heterogeneous clusters, stream processing, Apache Storm (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [B1D439986DB5]
http://linked.open...v/mistoKonaniAkce
  • Birmingham City University, Birmingham
http://linked.open...i/riv/mistoVydani
  • Birmingham
http://linked.open...i/riv/nazevZdroje
  • 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Rychlý, Marek
  • Smrž, Pavel
  • Škoda, Petr
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/CISIS.2014.94
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society
https://schema.org/isbn
  • 978-1-4799-4325-8
http://localhost/t...ganizacniJednotka
  • 26230
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