| ClassyTune: A Performance Auto-Tuner for Systems in the Cloud |
| Zhu, Yuqing1; Liu, Jianxun2
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| 2022
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| 发表期刊 | IEEE TRANSACTIONS ON CLOUD COMPUTING
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| ISSN | 2168-7161
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| 卷号 | 10期号:1页码:234-246 |
| 摘要 | Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. Due to the ever increasing number of parameters and complexity of systems, there is a necessity to automate performance tuning for the complicated systems in the cloud. The state-of-the-art tuning methods are adopting either the experience-driven tuning approach or the data-driven one. Data-driven tuning is attracting increasing attentions, as it has wider applicability. But existing data-driven methods cannot fully address the challenges of sample scarcity and high dimensionality simultaneously. We present ClassyTune, a data-driven automatic configuration tuning tool for cloud systems. ClassyTune exploits the machine learning model of classification for auto-tuning. This exploitation enables the induction of more training samples without increasing the input dimension. Experiments on seven popular systems in the cloud show that ClassyTune can effectively tune system performance to seven times higher for high-dimensional configuration space, outperforming expert tuning and the state-of-the-art auto-tuning solutions. We also describe a use case in which performance tuning enables the reduction of 33 percent computing resources needed to run an online stateless service. |
| 关键词 | Performance tuning
auto-tuning
autotuner
data-driven tuning
experience-driven tuning
performance modeling
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| DOI | 10.1109/TCC.2019.2936567
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| 收录类别 | SCI
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| 语种 | 英语
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| 资助项目 | National Key R&D Program of China[2016YFB1000201]
; National Natural Science Foundation of China[61420106013]
; Youth Innovation Promotion Association of Chinese Academy of Sciences
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| WOS研究方向 | Computer Science
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| WOS类目 | Computer Science, Information Systems
; Computer Science, Software Engineering
; Computer Science, Theory & Methods
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| WOS记录号 | WOS:000766635400019
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| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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| 引用统计 |
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| 文献类型 | 期刊论文
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| 条目标识符 | http://119.78.100.204/handle/2XEOYT63/18947
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| 专题 | 中国科学院计算技术研究所期刊论文_英文
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| 通讯作者 | Zhu, Yuqing |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.UTuned Sci & Technol Co Ltd, Beijing, Peoples R China
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推荐引用方式
GB/T 7714 |
Zhu, Yuqing,Liu, Jianxun. ClassyTune: A Performance Auto-Tuner for Systems in the Cloud[J]. IEEE TRANSACTIONS ON CLOUD COMPUTING,2022,10(1):234-246.
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| APA |
Zhu, Yuqing,&Liu, Jianxun.(2022).ClassyTune: A Performance Auto-Tuner for Systems in the Cloud.IEEE TRANSACTIONS ON CLOUD COMPUTING,10(1),234-246.
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| MLA |
Zhu, Yuqing,et al."ClassyTune: A Performance Auto-Tuner for Systems in the Cloud".IEEE TRANSACTIONS ON CLOUD COMPUTING 10.1(2022):234-246.
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