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...
Ausführliche Beschreibung
Autor*in: |
Mukherjee, Aradhita [verfasserIn] |
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2023 |
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© 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 - Springer US, 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 |
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DOI / URN: |
10.1007/s11227-022-05028-8 |
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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. | ||
650 | 4 | |a Distributed graph processing | |
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700 | 1 | |a Chaki, Nabendu |4 aut | |
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10.1007/s11227-022-05028-8 doi (DE-627)OLC2134644591 (DE-He213)s11227-022-05028-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Failure recovery Hierarchical clustering Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://doi.org/10.1007/s11227-022-05028-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 9 13 01 9383-9408 |
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10.1007/s11227-022-05028-8 doi (DE-627)OLC2134644591 (DE-He213)s11227-022-05028-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Failure recovery Hierarchical clustering Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://doi.org/10.1007/s11227-022-05028-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 9 13 01 9383-9408 |
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10.1007/s11227-022-05028-8 doi (DE-627)OLC2134644591 (DE-He213)s11227-022-05028-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Failure recovery Hierarchical clustering Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://doi.org/10.1007/s11227-022-05028-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 9 13 01 9383-9408 |
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10.1007/s11227-022-05028-8 doi (DE-627)OLC2134644591 (DE-He213)s11227-022-05028-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Failure recovery Hierarchical clustering Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://doi.org/10.1007/s11227-022-05028-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 9 13 01 9383-9408 |
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10.1007/s11227-022-05028-8 doi (DE-627)OLC2134644591 (DE-He213)s11227-022-05028-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Mukherjee, Aradhita verfasserin aut An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Failure recovery Hierarchical clustering Chaki, Rituparna aut Chaki, Nabendu aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 9 vom: 13. Jan., Seite 9383-9408 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:9 day:13 month:01 pages:9383-9408 https://doi.org/10.1007/s11227-022-05028-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 9 13 01 9383-9408 |
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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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title_short |
An unsupervised learning-guided multi-node failure-recovery model for distributed graph processing systems |
url |
https://doi.org/10.1007/s11227-022-05028-8 |
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Chaki, Rituparna Chaki, Nabendu |
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up_date |
2024-07-04T02:00:47.924Z |
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