PRESC <sup>2</sup>: Efficient self-reconfiguration of cache strategies for elastic caching platforms

ISCAS OpenIR

	PRESC ²: Efficient self-reconfiguration of cache strategies for elastic caching platforms
	Qin, Xiulei (1); Wang, Wei (1); Zhang, Wenbo (1); Wei, Jun (1); Zhao, Xin (1); Zhong, Hua (1); Huang, Tao (1); Qin, X.(qinxiulei08@otcaix.iscas.ac.cn)
	2014
发表期刊	Computing
ISSN	0010485X
卷号	96 期号:5 页码:415-451
摘要	Elastic caching platforms (ECPs) play an important role in accelerating the performance of Web applications. Several cache strategies have been proposed for ECPs to manage data access and distributions while maintaining the service availability. In our earlier research, we have demonstrated that there is no "one-fits-all" strategy for heterogeneous scenarios and the selection of the optimal strategy is related with workload patterns, cluster size and the number of concurrent users. In this paper, we present a new reconfiguration framework named PRESC². It applies machine learning approaches to determine an optimal cache strategy and supports online optimization of performance model through trace-driven simulation or semi-supervised classification. Besides, the authors also propose a robust cache entries synchronization algorithm and a new optimization mechanism to further lower the adaptation costs. In our experiments, we find that PRESC² improves the elasticity of ECPs and brings big performance gains when compared with static configurations. © 2013 Springer-Verlag Wien.; Elastic caching platforms (ECPs) play an important role in accelerating the performance of Web applications. Several cache strategies have been proposed for ECPs to manage data access and distributions while maintaining the service availability. In our earlier research, we have demonstrated that there is no "one-fits-all" strategy for heterogeneous scenarios and the selection of the optimal strategy is related with workload patterns, cluster size and the number of concurrent users. In this paper, we present a new reconfiguration framework named PRESC². It applies machine learning approaches to determine an optimal cache strategy and supports online optimization of performance model through trace-driven simulation or semi-supervised classification. Besides, the authors also propose a robust cache entries synchronization algorithm and a new optimization mechanism to further lower the adaptation costs. In our experiments, we find that PRESC² improves the elasticity of ECPs and brings big performance gains when compared with static configurations. © 2013 Springer-Verlag Wien.
收录类别	SCI ; EI
关键词	Elastic Caching Platform Cache Strategy Machine Learning Self-reconfiguration
部门归属	(1) TCSE, Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) State Key Laboratory of Computer Science, Beijing, China; (3) University of Chinese Academy of Sciences, Beijing, China
语种	英语
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16864
专题	中国科学院软件研究所
通讯作者	Qin, X.(qinxiulei08@otcaix.iscas.ac.cn)
推荐引用方式 GB/T 7714	Qin, Xiulei ,Wang, Wei ,Zhang, Wenbo ,et al. PRESC ²: Efficient self-reconfiguration of cache strategies for elastic caching platforms[J]. Computing,2014,96(5):415-451.
APA	Qin, Xiulei .,Wang, Wei .,Zhang, Wenbo .,Wei, Jun .,Zhao, Xin .,...&Qin, X..(2014).PRESC ²: Efficient self-reconfiguration of cache strategies for elastic caching platforms.Computing,96(5),415-451.
MLA	Qin, Xiulei ,et al."PRESC ²: Efficient self-reconfiguration of cache strategies for elastic caching platforms".Computing 96.5(2014):415-451.