An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems
Abstract Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple n...
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| Autor*in: |
Mukherjee, Aradhita [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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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| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 79(2023), 9 vom: 13. Jan., Seite 9383-9408 |
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| Übergeordnetes Werk: |
volume:79 ; year:2023 ; number:9 ; day:13 ; month:01 ; pages:9383-9408 |
| Links: |
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| DOI / URN: |
10.1007/s11227-022-05028-8 |
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| Katalog-ID: |
SPR050161431 |
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| 245 | 1 | 3 | |a An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems |
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| 520 | |a Abstract Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. | ||
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| 650 | 4 | |a Failure recovery |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Chaki, Nabendu |4 aut | |
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10.1007/s11227-022-05028-8 doi (DE-627)SPR050161431 (SPR)s11227-022-05028-8-e DE-627 ger DE-627 rakwb eng Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 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 2023. 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 Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. Distributed graph processing (dpeaa)DE-He213 Failure recovery (dpeaa)DE-He213 Hierarchical clustering (dpeaa)DE-He213 Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://dx.doi.org/10.1007/s11227-022-05028-8 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_206 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_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_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 79 2023 9 13 01 9383-9408 |
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10.1007/s11227-022-05028-8 doi (DE-627)SPR050161431 (SPR)s11227-022-05028-8-e DE-627 ger DE-627 rakwb eng Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 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 2023. 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 Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. Distributed graph processing (dpeaa)DE-He213 Failure recovery (dpeaa)DE-He213 Hierarchical clustering (dpeaa)DE-He213 Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://dx.doi.org/10.1007/s11227-022-05028-8 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_206 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_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_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 79 2023 9 13 01 9383-9408 |
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10.1007/s11227-022-05028-8 doi (DE-627)SPR050161431 (SPR)s11227-022-05028-8-e DE-627 ger DE-627 rakwb eng Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 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 2023. 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 Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. Distributed graph processing (dpeaa)DE-He213 Failure recovery (dpeaa)DE-He213 Hierarchical clustering (dpeaa)DE-He213 Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://dx.doi.org/10.1007/s11227-022-05028-8 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_206 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_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_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 79 2023 9 13 01 9383-9408 |
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10.1007/s11227-022-05028-8 doi (DE-627)SPR050161431 (SPR)s11227-022-05028-8-e DE-627 ger DE-627 rakwb eng Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 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 2023. 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 Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. Distributed graph processing (dpeaa)DE-He213 Failure recovery (dpeaa)DE-He213 Hierarchical clustering (dpeaa)DE-He213 Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://dx.doi.org/10.1007/s11227-022-05028-8 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_206 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_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_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 79 2023 9 13 01 9383-9408 |
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10.1007/s11227-022-05028-8 doi (DE-627)SPR050161431 (SPR)s11227-022-05028-8-e DE-627 ger DE-627 rakwb eng Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 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 2023. 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 Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. Distributed graph processing (dpeaa)DE-He213 Failure recovery (dpeaa)DE-He213 Hierarchical clustering (dpeaa)DE-He213 Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)271350202 (DE-600)1479917-0 1573-0484 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://dx.doi.org/10.1007/s11227-022-05028-8 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_206 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_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_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 79 2023 9 13 01 9383-9408 |
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unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems |
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An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems |
| abstract |
Abstract Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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 Big data applications based on graphs need to be scalable enough for handling immense growth in size of graphs, efficiently. Scalable graph processing typically handles the high workload by increasing the number of computing nodes. However, this increases the chances of single or multiple node (multi-node) failures. Failures may occur during normal job execution, as well as during recovery. Most of the systems for failure detection either follow checkpoint-based recovery which has high computation cost, or follows replication that has high memory overhead. In this work, we have proposed an unsupervised learning-based failure-recovery scheme for graph processing systems that detects different kinds of failures and allows node recovery within a shorter amount of time. It has been able to provide enhanced performance as compared to traditional failure-recovery models with respect to simultaneous recovery from single and multi-node failures, memory overload and computational latency. Evaluating its performance on four benchmark datasets has reinforced its strength and makes the proposed model completely fit in with the status quo. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. 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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An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems |
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https://dx.doi.org/10.1007/s11227-022-05028-8 |
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Chaki, Rituparna Chaki, Nabendu |
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Chaki, Rituparna Chaki, Nabendu |
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10.1007/s11227-022-05028-8 |
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2024-07-03T13:46:21.596Z |
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| score |
7.3985605 |