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001 978-3-319-73214-5
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008 180224s2018 gw | s |||| 0|eng d
020 _a9783319732145
_9978-3-319-73214-5
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_2bicssc
072 7 _aTEC009000
_2bisacsh
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082 0 4 _a006.3
_223
100 1 _aTan, Rong Kun Jason.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aOptimized Cloud Based Scheduling
_h[electronic resource] /
_cby Rong Kun Jason Tan, John A. Leong, Amandeep S. Sidhu.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIII, 99 p. 33 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aData, Semantics and Cloud Computing,
_x2524-6593 ;
_v759
500 _aAcceso multiusuario
505 0 _aIntroduction -- Background -- Benchmarking -- Computation of Large Datasets -- Optimized Online Scheduling Algorithms.
520 _aThis book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aComputational intelligence.
650 0 _aArtificial intelligence.
650 0 _aApplication software.
650 1 4 _aComputational Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T11014
650 2 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650 2 4 _aInformation Systems Applications (incl. Internet).
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18040
700 1 _aLeong, John A.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aSidhu, Amandeep S.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319732121
776 0 8 _iPrinted edition:
_z9783319732138
776 0 8 _iPrinted edition:
_z9783030103330
830 0 _aData, Semantics and Cloud Computing,
_x2524-6593 ;
_v759
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-73214-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c241904
_d241903