Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity

Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level...
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Autor*in:	Rasooli, Aysan [verfasserIn] Down, Douglas G. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Hadoop system Scheduling system Heterogeneous Hadoop

Übergeordnetes Werk:	Enthalten in: Journal of grid computing - Dordrecht : Springer Science + Business Media B.V., 2003, 12(2014), 3 vom: 22. Juli, Seite 499-519
Übergeordnetes Werk:	volume:12 ; year:2014 ; number:3 ; day:22 ; month:07 ; pages:499-519

Links:	Volltext

DOI / URN:	10.1007/s10723-014-9299-2

Katalog-ID:	SPR012754633

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allfields	10.1007/s10723-014-9299-2 doi (DE-627)SPR012754633 (SPR)s10723-014-9299-2-e DE-627 ger DE-627 rakwb eng 510 004 ASE 54.25 bkl 54.32 bkl Rasooli, Aysan verfasserin aut Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This paper studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for Hadoop systems. Finally, the proposed guidelines are evaluated in different Hadoop systems. Hadoop system (dpeaa)DE-He213 Scheduling system (dpeaa)DE-He213 Heterogeneous Hadoop (dpeaa)DE-He213 Down, Douglas G. verfasserin aut Enthalten in Journal of grid computing Dordrecht : Springer Science + Business Media B.V., 2003 12(2014), 3 vom: 22. Juli, Seite 499-519 (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:12 year:2014 number:3 day:22 month:07 pages:499-519 https://dx.doi.org/10.1007/s10723-014-9299-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 ASE 54.32 ASE AR 12 2014 3 22 07 499-519
spelling	10.1007/s10723-014-9299-2 doi (DE-627)SPR012754633 (SPR)s10723-014-9299-2-e DE-627 ger DE-627 rakwb eng 510 004 ASE 54.25 bkl 54.32 bkl Rasooli, Aysan verfasserin aut Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This paper studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for Hadoop systems. Finally, the proposed guidelines are evaluated in different Hadoop systems. Hadoop system (dpeaa)DE-He213 Scheduling system (dpeaa)DE-He213 Heterogeneous Hadoop (dpeaa)DE-He213 Down, Douglas G. verfasserin aut Enthalten in Journal of grid computing Dordrecht : Springer Science + Business Media B.V., 2003 12(2014), 3 vom: 22. Juli, Seite 499-519 (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:12 year:2014 number:3 day:22 month:07 pages:499-519 https://dx.doi.org/10.1007/s10723-014-9299-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 ASE 54.32 ASE AR 12 2014 3 22 07 499-519
allfields_unstemmed	10.1007/s10723-014-9299-2 doi (DE-627)SPR012754633 (SPR)s10723-014-9299-2-e DE-627 ger DE-627 rakwb eng 510 004 ASE 54.25 bkl 54.32 bkl Rasooli, Aysan verfasserin aut Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This paper studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for Hadoop systems. Finally, the proposed guidelines are evaluated in different Hadoop systems. Hadoop system (dpeaa)DE-He213 Scheduling system (dpeaa)DE-He213 Heterogeneous Hadoop (dpeaa)DE-He213 Down, Douglas G. verfasserin aut Enthalten in Journal of grid computing Dordrecht : Springer Science + Business Media B.V., 2003 12(2014), 3 vom: 22. Juli, Seite 499-519 (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:12 year:2014 number:3 day:22 month:07 pages:499-519 https://dx.doi.org/10.1007/s10723-014-9299-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 ASE 54.32 ASE AR 12 2014 3 22 07 499-519
allfieldsGer	10.1007/s10723-014-9299-2 doi (DE-627)SPR012754633 (SPR)s10723-014-9299-2-e DE-627 ger DE-627 rakwb eng 510 004 ASE 54.25 bkl 54.32 bkl Rasooli, Aysan verfasserin aut Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This paper studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for Hadoop systems. Finally, the proposed guidelines are evaluated in different Hadoop systems. Hadoop system (dpeaa)DE-He213 Scheduling system (dpeaa)DE-He213 Heterogeneous Hadoop (dpeaa)DE-He213 Down, Douglas G. verfasserin aut Enthalten in Journal of grid computing Dordrecht : Springer Science + Business Media B.V., 2003 12(2014), 3 vom: 22. Juli, Seite 499-519 (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:12 year:2014 number:3 day:22 month:07 pages:499-519 https://dx.doi.org/10.1007/s10723-014-9299-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 ASE 54.32 ASE AR 12 2014 3 22 07 499-519
allfieldsSound	10.1007/s10723-014-9299-2 doi (DE-627)SPR012754633 (SPR)s10723-014-9299-2-e DE-627 ger DE-627 rakwb eng 510 004 ASE 54.25 bkl 54.32 bkl Rasooli, Aysan verfasserin aut Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This paper studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for Hadoop systems. Finally, the proposed guidelines are evaluated in different Hadoop systems. Hadoop system (dpeaa)DE-He213 Scheduling system (dpeaa)DE-He213 Heterogeneous Hadoop (dpeaa)DE-He213 Down, Douglas G. verfasserin aut Enthalten in Journal of grid computing Dordrecht : Springer Science + Business Media B.V., 2003 12(2014), 3 vom: 22. Juli, Seite 499-519 (DE-627)359787843 (DE-600)2098457-1 1572-9184 nnns volume:12 year:2014 number:3 day:22 month:07 pages:499-519 https://dx.doi.org/10.1007/s10723-014-9299-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.25 ASE 54.32 ASE AR 12 2014 3 22 07 499-519
language	English
source	Enthalten in Journal of grid computing 12(2014), 3 vom: 22. Juli, Seite 499-519 volume:12 year:2014 number:3 day:22 month:07 pages:499-519
sourceStr	Enthalten in Journal of grid computing 12(2014), 3 vom: 22. Juli, Seite 499-519 volume:12 year:2014 number:3 day:22 month:07 pages:499-519
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Hadoop system Scheduling system Heterogeneous Hadoop
dewey-raw	510
isfreeaccess_bool	false
container_title	Journal of grid computing
authorswithroles_txt_mv	Rasooli, Aysan @@aut@@ Down, Douglas G. @@aut@@
publishDateDaySort_date	2014-07-22T00:00:00Z
hierarchy_top_id	359787843
dewey-sort	3510
id	SPR012754633
language_de	englisch
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author	Rasooli, Aysan
spellingShingle	Rasooli, Aysan ddc 510 bkl 54.25 bkl 54.32 misc Hadoop system misc Scheduling system misc Heterogeneous Hadoop Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity
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topic_title	510 004 ASE 54.25 bkl 54.32 bkl Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity Hadoop system (dpeaa)DE-He213 Scheduling system (dpeaa)DE-He213 Heterogeneous Hadoop (dpeaa)DE-He213
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title	Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity
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title_full	Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity
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title_sort	guidelines for selecting hadoop schedulers based on system heterogeneity
title_auth	Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity
abstract	Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This paper studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for Hadoop systems. Finally, the proposed guidelines are evaluated in different Hadoop systems.
abstractGer	Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This paper studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for Hadoop systems. Finally, the proposed guidelines are evaluated in different Hadoop systems.
abstract_unstemmed	Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This paper studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop). Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for Hadoop systems. Finally, the proposed guidelines are evaluated in different Hadoop systems.
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title_short	Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity
url	https://dx.doi.org/10.1007/s10723-014-9299-2
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doi_str	10.1007/s10723-014-9299-2
up_date	2024-07-03T15:05:55.223Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR012754633</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110235412.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2014 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10723-014-9299-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR012754633</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10723-014-9299-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.25</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rasooli, Aysan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Guidelines for Selecting Hadoop Schedulers Based on System Heterogeneity</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Hadoop has been developed as a solution for performing large-scale data-parallel applications in Cloud computing. 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