COSHH: A classification and optimization based scheduler for heterogeneous Hadoop systems
A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rasooli, Aysan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Surgeon-patient matching based on pairwise comparisons information for elective surgery - Jiang, Yan-Ping ELSEVIER, 2020, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:36 ; year:2014 ; pages:1-15 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.future.2014.01.002 |
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| 520 | |a A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. | ||
| 520 | |a A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. | ||
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10.1016/j.future.2014.01.002 doi GBVA2014004000011.pica (DE-627)ELV027869083 (ELSEVIER)S0167-739X(14)00007-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 004 VZ 85.35 bkl 54.80 bkl Rasooli, Aysan verfasserin aut COSHH: A classification and optimization based scheduler for heterogeneous Hadoop systems 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. Hadoop system Elsevier Scheduling system Elsevier Heterogeneous Hadoop Elsevier Down, Douglas G. oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:36 year:2014 pages:1-15 extent:15 https://doi.org/10.1016/j.future.2014.01.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 36 2014 1-15 15 045F 004 |
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10.1016/j.future.2014.01.002 doi GBVA2014004000011.pica (DE-627)ELV027869083 (ELSEVIER)S0167-739X(14)00007-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 004 VZ 85.35 bkl 54.80 bkl Rasooli, Aysan verfasserin aut COSHH: A classification and optimization based scheduler for heterogeneous Hadoop systems 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. Hadoop system Elsevier Scheduling system Elsevier Heterogeneous Hadoop Elsevier Down, Douglas G. oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:36 year:2014 pages:1-15 extent:15 https://doi.org/10.1016/j.future.2014.01.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 36 2014 1-15 15 045F 004 |
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10.1016/j.future.2014.01.002 doi GBVA2014004000011.pica (DE-627)ELV027869083 (ELSEVIER)S0167-739X(14)00007-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 004 VZ 85.35 bkl 54.80 bkl Rasooli, Aysan verfasserin aut COSHH: A classification and optimization based scheduler for heterogeneous Hadoop systems 2014transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. Hadoop system Elsevier Scheduling system Elsevier Heterogeneous Hadoop Elsevier Down, Douglas G. oth Enthalten in Elsevier Science Jiang, Yan-Ping ELSEVIER Surgeon-patient matching based on pairwise comparisons information for elective surgery 2020 Amsterdam [u.a.] (DE-627)ELV004280385 volume:36 year:2014 pages:1-15 extent:15 https://doi.org/10.1016/j.future.2014.01.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 85.35 Fertigung VZ 54.80 Angewandte Informatik VZ AR 36 2014 1-15 15 045F 004 |
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COSHH: A classification and optimization based scheduler for heterogeneous Hadoop systems |
| abstract |
A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. |
| abstractGer |
A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. |
| abstract_unstemmed |
A Hadoop system provides execution and multiplexing of many tasks in a common datacenter. There is a rising demand for sharing Hadoop clusters amongst various users, which leads to increasing system heterogeneity. However, heterogeneity is a neglected issue in most Hadoop schedulers. In this work we design and implement a new Hadoop scheduling system, named COSHH, which considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the mean completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, our proposed scheduler also achieves competitive performance under minimum share satisfaction, fairness and locality metrics with respect to other well-known Hadoop schedulers. |
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GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
COSHH: A classification and optimization based scheduler for heterogeneous Hadoop systems |
| url |
https://doi.org/10.1016/j.future.2014.01.002 |
| remote_bool |
true |
| author2 |
Down, Douglas G. |
| author2Str |
Down, Douglas G. |
| ppnlink |
ELV004280385 |
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| hochschulschrift_bool |
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| doi_str |
10.1016/j.future.2014.01.002 |
| up_date |
2024-07-06T17:20:40.393Z |
| _version_ |
1803851074522578944 |
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| score |
7.3998156 |