elasticat: a load rebalancing framework for cloud-based key-value stores

ISCAS OpenIR

	elasticat: a load rebalancing framework for cloud-based key-value stores
	Qin Xiulei; Wang Wei; Zhang Wenbo; Wei Jun; Zhao Xin; Huang Tao
	2012
会议名称	2012 19th International Conference on High Performance Computing, HiPC 2012
会议录名称	2012 19th International Conference on High Performance Computing, HiPC 2012
页码	-
会议日期	December 18, 2012 - December 21, 2012
会议地点	Pune, India
收录类别	EI
ISBN	9781467323703
部门归属	(1) Institute of Software Chinese Academy of Sciences Beijing China; (2) State Key Laboratory of Computer Science Beijing China; (3) Graduate University of Chinese Academy of Sciences Beijing China
摘要	The problem of load rebalancing is an important issue for cloud-based key-value stores. However, the new virtualization environment and the store's stateful feature make this classical issue more challenging. In this paper, we build a new load rebalancing framework for cloud-based key-value stores, namely ElastiCat. It can be used for auto reconfiguring the store system with minimal costs and no disruption to the availability of the service. To evaluate and minimize the rebalancing costs, we firstly build two interference-aware prediction models to predict the data migration time and performance impact for each action using statistical machine learning and then create a cost model to strike a right balance between them. A cost-aware rebalancing algorithm is designed to utilize the cost model and balance rate to create a rebalancing plan and guide the choice of possible rebalancing actions. To maintain the availability of storage service, we propose a lightweight piggy-back based data access protocol. Finally, we demonstrate the effectiveness of the framework as well as the cost model using YCSB. © 2012 IEEE.; The problem of load rebalancing is an important issue for cloud-based key-value stores. However, the new virtualization environment and the store's stateful feature make this classical issue more challenging. In this paper, we build a new load rebalancing framework for cloud-based key-value stores, namely ElastiCat. It can be used for auto reconfiguring the store system with minimal costs and no disruption to the availability of the service. To evaluate and minimize the rebalancing costs, we firstly build two interference-aware prediction models to predict the data migration time and performance impact for each action using statistical machine learning and then create a cost model to strike a right balance between them. A cost-aware rebalancing algorithm is designed to utilize the cost model and balance rate to create a rebalancing plan and guide the choice of possible rebalancing actions. To maintain the availability of storage service, we propose a lightweight piggy-back based data access protocol. Finally, we demonstrate the effectiveness of the framework as well as the cost model using YCSB. © 2012 IEEE.
关键词	Computer Science
主办者	IEEE Comput. Soc. Tech. Comm. Parallel Process. (TCPP); ACM; CSIR; IISER; IISc
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15980
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Qin Xiulei,Wang Wei,Zhang Wenbo,et al. elasticat: a load rebalancing framework for cloud-based key-value stores[C],2012:-.