Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster

Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem....
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Autor*in:	Heo, Seoungbeom [verfasserIn] Kang, Dae-Cheol Jang, Hyeounji Lee, Hyeock-Jin Cho, Minkyoung Kim, Jik-Soo

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Hadoop YARN Deep Learning Lustre

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 26(2022), 5 vom: 17. Nov., Seite 2851-2864
Übergeordnetes Werk:	volume:26 ; year:2022 ; number:5 ; day:17 ; month:11 ; pages:2851-2864

Links:	Volltext

DOI / URN:	10.1007/s10586-022-03799-6

Katalog-ID:	SPR052883167

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allfields	10.1007/s10586-022-03799-6 doi (DE-627)SPR052883167 (SPR)s10586-022-03799-6-e DE-627 ger DE-627 rakwb eng Heo, Seoungbeom verfasserin aut Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. In this paper, we present comprehensive techniques that can effectively support multiple deep learning applications in a Hadoop YARN cluster by leveraging fine-grained GPU over-provisioning policy and a high-performance parallel file system for data staging which can improve the overall system throughput. Through our extensive experiments based on the representative deep learning workloads, we demonstrate that MeLoN can make an effective convergence of deep learning and the big data platform Hadoop by employing YARN-based resource allocation and execution mechanisms for running distributed deep learning applications. We believe that MeLoN can bring many additional interesting research issues including profiling of expected GPU memory usages of deep learning applications, supporting more complicated deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. Hadoop (dpeaa)DE-He213 YARN (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 Lustre (dpeaa)DE-He213 Kang, Dae-Cheol aut Jang, Hyeounji aut Lee, Hyeock-Jin aut Cho, Minkyoung aut Kim, Jik-Soo (orcid)0000-0002-0104-4617 aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 26(2022), 5 vom: 17. Nov., Seite 2851-2864 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:26 year:2022 number:5 day:17 month:11 pages:2851-2864 https://dx.doi.org/10.1007/s10586-022-03799-6 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 5 17 11 2851-2864
spelling	10.1007/s10586-022-03799-6 doi (DE-627)SPR052883167 (SPR)s10586-022-03799-6-e DE-627 ger DE-627 rakwb eng Heo, Seoungbeom verfasserin aut Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. In this paper, we present comprehensive techniques that can effectively support multiple deep learning applications in a Hadoop YARN cluster by leveraging fine-grained GPU over-provisioning policy and a high-performance parallel file system for data staging which can improve the overall system throughput. Through our extensive experiments based on the representative deep learning workloads, we demonstrate that MeLoN can make an effective convergence of deep learning and the big data platform Hadoop by employing YARN-based resource allocation and execution mechanisms for running distributed deep learning applications. We believe that MeLoN can bring many additional interesting research issues including profiling of expected GPU memory usages of deep learning applications, supporting more complicated deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. Hadoop (dpeaa)DE-He213 YARN (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 Lustre (dpeaa)DE-He213 Kang, Dae-Cheol aut Jang, Hyeounji aut Lee, Hyeock-Jin aut Cho, Minkyoung aut Kim, Jik-Soo (orcid)0000-0002-0104-4617 aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 26(2022), 5 vom: 17. Nov., Seite 2851-2864 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:26 year:2022 number:5 day:17 month:11 pages:2851-2864 https://dx.doi.org/10.1007/s10586-022-03799-6 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 5 17 11 2851-2864
allfields_unstemmed	10.1007/s10586-022-03799-6 doi (DE-627)SPR052883167 (SPR)s10586-022-03799-6-e DE-627 ger DE-627 rakwb eng Heo, Seoungbeom verfasserin aut Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. In this paper, we present comprehensive techniques that can effectively support multiple deep learning applications in a Hadoop YARN cluster by leveraging fine-grained GPU over-provisioning policy and a high-performance parallel file system for data staging which can improve the overall system throughput. Through our extensive experiments based on the representative deep learning workloads, we demonstrate that MeLoN can make an effective convergence of deep learning and the big data platform Hadoop by employing YARN-based resource allocation and execution mechanisms for running distributed deep learning applications. We believe that MeLoN can bring many additional interesting research issues including profiling of expected GPU memory usages of deep learning applications, supporting more complicated deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. Hadoop (dpeaa)DE-He213 YARN (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 Lustre (dpeaa)DE-He213 Kang, Dae-Cheol aut Jang, Hyeounji aut Lee, Hyeock-Jin aut Cho, Minkyoung aut Kim, Jik-Soo (orcid)0000-0002-0104-4617 aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 26(2022), 5 vom: 17. Nov., Seite 2851-2864 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:26 year:2022 number:5 day:17 month:11 pages:2851-2864 https://dx.doi.org/10.1007/s10586-022-03799-6 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 5 17 11 2851-2864
allfieldsGer	10.1007/s10586-022-03799-6 doi (DE-627)SPR052883167 (SPR)s10586-022-03799-6-e DE-627 ger DE-627 rakwb eng Heo, Seoungbeom verfasserin aut Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. In this paper, we present comprehensive techniques that can effectively support multiple deep learning applications in a Hadoop YARN cluster by leveraging fine-grained GPU over-provisioning policy and a high-performance parallel file system for data staging which can improve the overall system throughput. Through our extensive experiments based on the representative deep learning workloads, we demonstrate that MeLoN can make an effective convergence of deep learning and the big data platform Hadoop by employing YARN-based resource allocation and execution mechanisms for running distributed deep learning applications. We believe that MeLoN can bring many additional interesting research issues including profiling of expected GPU memory usages of deep learning applications, supporting more complicated deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. Hadoop (dpeaa)DE-He213 YARN (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 Lustre (dpeaa)DE-He213 Kang, Dae-Cheol aut Jang, Hyeounji aut Lee, Hyeock-Jin aut Cho, Minkyoung aut Kim, Jik-Soo (orcid)0000-0002-0104-4617 aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 26(2022), 5 vom: 17. Nov., Seite 2851-2864 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:26 year:2022 number:5 day:17 month:11 pages:2851-2864 https://dx.doi.org/10.1007/s10586-022-03799-6 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 5 17 11 2851-2864
allfieldsSound	10.1007/s10586-022-03799-6 doi (DE-627)SPR052883167 (SPR)s10586-022-03799-6-e DE-627 ger DE-627 rakwb eng Heo, Seoungbeom verfasserin aut Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. In this paper, we present comprehensive techniques that can effectively support multiple deep learning applications in a Hadoop YARN cluster by leveraging fine-grained GPU over-provisioning policy and a high-performance parallel file system for data staging which can improve the overall system throughput. Through our extensive experiments based on the representative deep learning workloads, we demonstrate that MeLoN can make an effective convergence of deep learning and the big data platform Hadoop by employing YARN-based resource allocation and execution mechanisms for running distributed deep learning applications. We believe that MeLoN can bring many additional interesting research issues including profiling of expected GPU memory usages of deep learning applications, supporting more complicated deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. Hadoop (dpeaa)DE-He213 YARN (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 Lustre (dpeaa)DE-He213 Kang, Dae-Cheol aut Jang, Hyeounji aut Lee, Hyeock-Jin aut Cho, Minkyoung aut Kim, Jik-Soo (orcid)0000-0002-0104-4617 aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 26(2022), 5 vom: 17. Nov., Seite 2851-2864 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:26 year:2022 number:5 day:17 month:11 pages:2851-2864 https://dx.doi.org/10.1007/s10586-022-03799-6 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2022 5 17 11 2851-2864
language	English
source	Enthalten in Cluster computing 26(2022), 5 vom: 17. Nov., Seite 2851-2864 volume:26 year:2022 number:5 day:17 month:11 pages:2851-2864
sourceStr	Enthalten in Cluster computing 26(2022), 5 vom: 17. Nov., Seite 2851-2864 volume:26 year:2022 number:5 day:17 month:11 pages:2851-2864
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container_title	Cluster computing
authorswithroles_txt_mv	Heo, Seoungbeom @@aut@@ Kang, Dae-Cheol @@aut@@ Jang, Hyeounji @@aut@@ Lee, Hyeock-Jin @@aut@@ Cho, Minkyoung @@aut@@ Kim, Jik-Soo @@aut@@
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author	Heo, Seoungbeom
spellingShingle	Heo, Seoungbeom misc Hadoop misc YARN misc Deep Learning misc Lustre Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster
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topic_title	Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster Hadoop (dpeaa)DE-He213 YARN (dpeaa)DE-He213 Deep Learning (dpeaa)DE-He213 Lustre (dpeaa)DE-He213
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title	Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster
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title_full	Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster
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title_sort	comprehensive techniques for multi-tenant deep learning framework on a hadoop yarn cluster
title_auth	Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster
abstract	Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. In this paper, we present comprehensive techniques that can effectively support multiple deep learning applications in a Hadoop YARN cluster by leveraging fine-grained GPU over-provisioning policy and a high-performance parallel file system for data staging which can improve the overall system throughput. Through our extensive experiments based on the representative deep learning workloads, we demonstrate that MeLoN can make an effective convergence of deep learning and the big data platform Hadoop by employing YARN-based resource allocation and execution mechanisms for running distributed deep learning applications. We believe that MeLoN can bring many additional interesting research issues including profiling of expected GPU memory usages of deep learning applications, supporting more complicated deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. In this paper, we present comprehensive techniques that can effectively support multiple deep learning applications in a Hadoop YARN cluster by leveraging fine-grained GPU over-provisioning policy and a high-performance parallel file system for data staging which can improve the overall system throughput. Through our extensive experiments based on the representative deep learning workloads, we demonstrate that MeLoN can make an effective convergence of deep learning and the big data platform Hadoop by employing YARN-based resource allocation and execution mechanisms for running distributed deep learning applications. We believe that MeLoN can bring many additional interesting research issues including profiling of expected GPU memory usages of deep learning applications, supporting more complicated deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. In this paper, we present comprehensive techniques that can effectively support multiple deep learning applications in a Hadoop YARN cluster by leveraging fine-grained GPU over-provisioning policy and a high-performance parallel file system for data staging which can improve the overall system throughput. Through our extensive experiments based on the representative deep learning workloads, we demonstrate that MeLoN can make an effective convergence of deep learning and the big data platform Hadoop by employing YARN-based resource allocation and execution mechanisms for running distributed deep learning applications. We believe that MeLoN can bring many additional interesting research issues including profiling of expected GPU memory usages of deep learning applications, supporting more complicated deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Comprehensive techniques for multi-tenant deep learning framework on a Hadoop YARN cluster
url	https://dx.doi.org/10.1007/s10586-022-03799-6
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doi_str	10.1007/s10586-022-03799-6
up_date	2024-07-03T15:28:11.538Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We have designed and implemented a new data processing framework called “MeLoN” (Multi-tenant dEep Learning framework On yarN) which aims to effectively support distributed deep learning applications that can show another type of data-intensive workloads in the YARN-based Hadoop ecosystem. MeLoN is developed as one of Hadoop YARN applications so that it can transparently co-host existing deep learning applications with other data processing workflows. 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