on-line cache strategy reconfiguration for elastic caching platform: a machine learning approach

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	on-line cache strategy reconfiguration for elastic caching platform: a machine learning approach
	Qin Xiulei; Zhang Wenbo; Wang Wei; Wei Jun; Zhong Hua; Huang Tao
	2011
会议名称	35th Annual IEEE International Computer Software and Applications Conference, COMPSAC 2011
会议录名称	Proceedings - International Computer Software and Applications Conference
页码	523-534
会议日期	July 18, 2
会议地点	Munich, Germany
收录类别	EI ; ISTP
ISSN	0730-3157
ISBN	9780769544397
部门归属	(1) Institute of Software Chinese Academy of Sciences China; (2) State Key Laboratory of Computer Science China; (3) Graduate University of Chinese Academy of Sciences Beijing China
摘要	Cloud computing provide scalability and high availability for web applications using such techniques as distributed caching and clustering. As one database offloading strategy, elastic caching platforms (ECPs) are introduced to speed up the performance or handle application state management with fault tolerance. Several cache strtegies for ECPs have been proposed, say replicated strategy, partitioned strategy and near strategy. We first evaluate the impact of the three cache strategies using the TPC-W benchmark and find that there is no single cache strategy suitable for all conditions, the selection of the best strategy is related with workload patterns, cluster size and the number of concurrent users. This raises the question of when and how the cache strategy should be reconfigured as the condition varies which has received comparatively less attention. In this paper, we present a machine learning based approach to solving this problem. The key features of the approach are off-line training coupled with on-line system monitoring and robust synchronization process after triggering a reconfiguration, at the same time the performance model is periodically updated. More explicitly, first a rule set used to identify which cache strategy is optimal under the current condition are trained with the system statistics and performance results. We then introduce a framework to switch the cache strategy on-line as the workload varies and keep its overhead to acceptable levels. Finally, we illustrate the advantages of this approach by carrying out a set of experiments. © 2011 IEEE.; Cloud computing provide scalability and high availability for web applications using such techniques as distributed caching and clustering. As one database offloading strategy, elastic caching platforms (ECPs) are introduced to speed up the performance or handle application state management with fault tolerance. Several cache strtegies for ECPs have been proposed, say replicated strategy, partitioned strategy and near strategy. We first evaluate the impact of the three cache strategies using the TPC-W benchmark and find that there is no single cache strategy suitable for all conditions, the selection of the best strategy is related with workload patterns, cluster size and the number of concurrent users. This raises the question of when and how the cache strategy should be reconfigured as the condition varies which has received comparatively less attention. In this paper, we present a machine learning based approach to solving this problem. The key features of the approach are off-line training coupled with on-line system monitoring and robust synchronization process after triggering a reconfiguration, at the same time the performance model is periodically updated. More explicitly, first a rule set used to identify which cache strategy is optimal under the current condition are trained with the system statistics and performance results. We then introduce a framework to switch the cache strategy on-line as the workload varies and keep its overhead to acceptable levels. Finally, we illustrate the advantages of this approach by carrying out a set of experiments. © 2011 IEEE.
关键词	Cloud Computing Fault Tolerance Learning Systems Scalability User Interfaces
主办者	IEEE; IEEE Computer Society
学科领域	Computer Science
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16201
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Qin Xiulei,Zhang Wenbo,Wang Wei,et al. on-line cache strategy reconfiguration for elastic caching platform: a machine learning approach[C],2011:523-534.