构建自动演进的天文大数据负载模型

Huajin Wang; Meng Wan; Rui Han; Wei Ren; Haiming Zhang; Jianhui Li

doi:10.16182/j.issn1004731x.joss.201809009

构建自动演进的天文大数据负载模型

Translated title of the contribution: Towards the Automatic Evolution of Workload Models in Large-scale Astronomical Data Management

Huajin Wang, Meng Wan, Rui Han, Wei Ren, Haiming Zhang, Jianhui Li^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

The benchmark's guiding role in system selection/optimization requires its workload model has the ability to: Run on various systems of the target application scenario (be portable); Reflect the typical tasks' characteristics and data access patterns (be representative). The emerging systems and tasks in large-scale astronomical data management field have led workload models constructed by existing methods to be prone to lose portability and representativeness. An automatic evolutionary workload modeling method has been proposed: Abstract operations are used to keep the workload model’s portability; Automatic workload log analytics are used to keep the workload model’s representativeness. The feasibility of this method is verified by a cluster optimization case.

Translated title of the contribution	Towards the Automatic Evolution of Workload Models in Large-scale Astronomical Data Management
Original language	Chinese (Traditional)
Pages (from-to)	3293-3305
Number of pages	13
Journal	Xitong Fangzhen Xuebao / Journal of System Simulation
Volume	30
Issue number	9
DOIs	https://doi.org/10.16182/j.issn1004731x.joss.201809009
Publication status	Published - 8 Sept 2018
Externally published	Yes

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Cite this

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title = "构建自动演进的天文大数据负载模型",

abstract = "The benchmark's guiding role in system selection/optimization requires its workload model has the ability to: Run on various systems of the target application scenario (be portable); Reflect the typical tasks' characteristics and data access patterns (be representative). The emerging systems and tasks in large-scale astronomical data management field have led workload models constructed by existing methods to be prone to lose portability and representativeness. An automatic evolutionary workload modeling method has been proposed: Abstract operations are used to keep the workload model{\textquoteright}s portability; Automatic workload log analytics are used to keep the workload model{\textquoteright}s representativeness. The feasibility of this method is verified by a cluster optimization case.",

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author = "Huajin Wang and Meng Wan and Rui Han and Wei Ren and Haiming Zhang and Jianhui Li",

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AU - Wang, Huajin

AU - Wan, Meng

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AU - Ren, Wei

AU - Zhang, Haiming

AU - Li, Jianhui

PY - 2018/9/8

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AB - The benchmark's guiding role in system selection/optimization requires its workload model has the ability to: Run on various systems of the target application scenario (be portable); Reflect the typical tasks' characteristics and data access patterns (be representative). The emerging systems and tasks in large-scale astronomical data management field have led workload models constructed by existing methods to be prone to lose portability and representativeness. An automatic evolutionary workload modeling method has been proposed: Abstract operations are used to keep the workload model’s portability; Automatic workload log analytics are used to keep the workload model’s representativeness. The feasibility of this method is verified by a cluster optimization case.

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KW - Query optimization

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构建自动演进的天文大数据负载模型

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