Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment

Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finis...
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Autor*in:	Ramanathan, Ramakrishnan [verfasserIn] Latha, B. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Cloud Computing Hadoop MapReduce Performance Model Resource Provisioning Efficiency

Übergeordnetes Werk:	Enthalten in: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 22(2018), Suppl 6 vom: 26. Feb., Seite 14061-14071
Übergeordnetes Werk:	volume:22 ; year:2018 ; number:Suppl 6 ; day:26 ; month:02 ; pages:14061-14071

Links:	Volltext

DOI / URN:	10.1007/s10586-018-2234-8

Katalog-ID:	SPR011524669

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520			\|a Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results.
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matchkey_str	article:15737543:2018----::oadotmleorervsoigohdompeueossnsaeusrtgadtpromn
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publishDate	2018
allfields	10.1007/s10586-018-2234-8 doi (DE-627)SPR011524669 (SPR)s10586-018-2234-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Ramanathan, Ramakrishnan verfasserin aut Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results. Cloud Computing (dpeaa)DE-He213 Hadoop MapReduce (dpeaa)DE-He213 Performance Model (dpeaa)DE-He213 Resource Provisioning (dpeaa)DE-He213 Efficiency (dpeaa)DE-He213 Latha, B. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. Feb., Seite 14061-14071 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:02 pages:14061-14071 https://dx.doi.org/10.1007/s10586-018-2234-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 26 02 14061-14071
spelling	10.1007/s10586-018-2234-8 doi (DE-627)SPR011524669 (SPR)s10586-018-2234-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Ramanathan, Ramakrishnan verfasserin aut Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results. Cloud Computing (dpeaa)DE-He213 Hadoop MapReduce (dpeaa)DE-He213 Performance Model (dpeaa)DE-He213 Resource Provisioning (dpeaa)DE-He213 Efficiency (dpeaa)DE-He213 Latha, B. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. Feb., Seite 14061-14071 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:02 pages:14061-14071 https://dx.doi.org/10.1007/s10586-018-2234-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 26 02 14061-14071
allfields_unstemmed	10.1007/s10586-018-2234-8 doi (DE-627)SPR011524669 (SPR)s10586-018-2234-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Ramanathan, Ramakrishnan verfasserin aut Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results. Cloud Computing (dpeaa)DE-He213 Hadoop MapReduce (dpeaa)DE-He213 Performance Model (dpeaa)DE-He213 Resource Provisioning (dpeaa)DE-He213 Efficiency (dpeaa)DE-He213 Latha, B. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. Feb., Seite 14061-14071 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:02 pages:14061-14071 https://dx.doi.org/10.1007/s10586-018-2234-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 26 02 14061-14071
allfieldsGer	10.1007/s10586-018-2234-8 doi (DE-627)SPR011524669 (SPR)s10586-018-2234-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Ramanathan, Ramakrishnan verfasserin aut Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results. Cloud Computing (dpeaa)DE-He213 Hadoop MapReduce (dpeaa)DE-He213 Performance Model (dpeaa)DE-He213 Resource Provisioning (dpeaa)DE-He213 Efficiency (dpeaa)DE-He213 Latha, B. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. Feb., Seite 14061-14071 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:02 pages:14061-14071 https://dx.doi.org/10.1007/s10586-018-2234-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 26 02 14061-14071
allfieldsSound	10.1007/s10586-018-2234-8 doi (DE-627)SPR011524669 (SPR)s10586-018-2234-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Ramanathan, Ramakrishnan verfasserin aut Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results. Cloud Computing (dpeaa)DE-He213 Hadoop MapReduce (dpeaa)DE-He213 Performance Model (dpeaa)DE-He213 Resource Provisioning (dpeaa)DE-He213 Efficiency (dpeaa)DE-He213 Latha, B. verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2018), Suppl 6 vom: 26. Feb., Seite 14061-14071 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2018 number:Suppl 6 day:26 month:02 pages:14061-14071 https://dx.doi.org/10.1007/s10586-018-2234-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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 54.50 ASE 54.32 ASE 54.25 ASE AR 22 2018 Suppl 6 26 02 14061-14071
language	English
source	Enthalten in Cluster computing 22(2018), Suppl 6 vom: 26. Feb., Seite 14061-14071 volume:22 year:2018 number:Suppl 6 day:26 month:02 pages:14061-14071
sourceStr	Enthalten in Cluster computing 22(2018), Suppl 6 vom: 26. Feb., Seite 14061-14071 volume:22 year:2018 number:Suppl 6 day:26 month:02 pages:14061-14071
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topic_facet	Cloud Computing Hadoop MapReduce Performance Model Resource Provisioning Efficiency
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container_title	Cluster computing
authorswithroles_txt_mv	Ramanathan, Ramakrishnan @@aut@@ Latha, B. @@aut@@
publishDateDaySort_date	2018-02-26T00:00:00Z
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author	Ramanathan, Ramakrishnan
spellingShingle	Ramanathan, Ramakrishnan ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Cloud Computing misc Hadoop MapReduce misc Performance Model misc Resource Provisioning misc Efficiency Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment
authorStr	Ramanathan, Ramakrishnan
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topic_title	004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment Cloud Computing (dpeaa)DE-He213 Hadoop MapReduce (dpeaa)DE-He213 Performance Model (dpeaa)DE-He213 Resource Provisioning (dpeaa)DE-He213 Efficiency (dpeaa)DE-He213
topic	ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Cloud Computing misc Hadoop MapReduce misc Performance Model misc Resource Provisioning misc Efficiency
topic_unstemmed	ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Cloud Computing misc Hadoop MapReduce misc Performance Model misc Resource Provisioning misc Efficiency
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title	Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment
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title_full	Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment
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title_sort	towards optimal resource provisioning for hadoop-mapreduce jobs using scale-out strategy and its performance analysis in private cloud environment
title_auth	Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment
abstract	Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results.
abstractGer	Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results.
abstract_unstemmed	Abstract Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results.
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container_issue	Suppl 6
title_short	Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment
url	https://dx.doi.org/10.1007/s10586-018-2234-8
remote_bool	true
author2	Latha, B.
author2Str	Latha, B.
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doi_str	10.1007/s10586-018-2234-8
up_date	2024-07-03T23:10:16.232Z
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