ClassyTune: A Performance Auto-Tuner for Systems in the Cloud

doi:10.1109/TCC.2019.2936567

	ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
	Zhu, Yuqing 1; Liu, Jianxun 2
	2022
发表期刊	IEEE TRANSACTIONS ON CLOUD COMPUTING
ISSN	2168-7161
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
DOI	10.1109/TCC.2019.2936567
收录类别	SCI
语种	英语
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
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000766635400019
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18947
专题	中国科学院计算技术研究所期刊论文_英文
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
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.
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.
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.