optimizing data migration for cloud-based key-value stores

ISCAS OpenIR

	optimizing data migration for cloud-based key-value stores
	Qin Xiulei; Zhang Wenbo; Wang Wei; Wei Jun; Zhao Xin; Huang Tao
	2012
会议名称	21st ACM International Conference on Information and Knowledge Management, CIKM 2012
会议录名称	ACM International Conference Proceeding Series
页码	2204-2208
会议日期	October 29, 2012 - November 2, 2012
会议地点	Maui, HI, United states
收录类别	EI
ISBN	9781450311564
部门归属	(1) 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
摘要	As one database offloading strategy, elastic key-value stores are often introduced to speed up the application performance with dynamic scalability. Since the workload is varied, efficient data migration with minimal impact in service is critical for the issue of elasticity and scalability. However, due to the new virtualization technology, real-time and low-latency requirements, data migration within cloud-based key-value stores has to face new challenges: effects of VM interference, and the need to trade off between the two ingredients of migration cost, namely migration time and performance impact. To fulfill these challenges, in this paper we explore a new approach to optimize the data migration. Explicitly, we build two interference-aware models to predict the migration time and performance impact for each migration action using statistical machine learning, and then create a cost model to strike a balance between the two ingredients. Using the load rebalancing scenario as a case study, we have designed one cost-aware migration algorithm that utilizes the cost model to guide the choice of possible migration actions. Finally, we demonstrate the effectiveness of the approach using Yahoo! Cloud Serving Benchmark (YCSB). © 2012 ACM.; As one database offloading strategy, elastic key-value stores are often introduced to speed up the application performance with dynamic scalability. Since the workload is varied, efficient data migration with minimal impact in service is critical for the issue of elasticity and scalability. However, due to the new virtualization technology, real-time and low-latency requirements, data migration within cloud-based key-value stores has to face new challenges: effects of VM interference, and the need to trade off between the two ingredients of migration cost, namely migration time and performance impact. To fulfill these challenges, in this paper we explore a new approach to optimize the data migration. Explicitly, we build two interference-aware models to predict the migration time and performance impact for each migration action using statistical machine learning, and then create a cost model to strike a balance between the two ingredients. Using the load rebalancing scenario as a case study, we have designed one cost-aware migration algorithm that utilizes the cost model to guide the choice of possible migration actions. Finally, we demonstrate the effectiveness of the approach using Yahoo! Cloud Serving Benchmark (YCSB). © 2012 ACM.
关键词	Elasticity Knowledge Management Optimization Scalability
主办者	Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEB
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15888
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. optimizing data migration for cloud-based key-value stores[C],2012:2204-2208.