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Title:
optimizing data migration for cloud-based key-value stores
Author: Qin Xiulei ; Zhang Wenbo ; Wang Wei ; Wei Jun ; Zhao Xin ; Huang Tao
Source: ACM International Conference Proceeding Series
Conference Name: 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Conference Date: October 29, 2012 - November 2, 2012
Issued Date: 2012
Conference Place: Maui, HI, United states
Keyword: Elasticity ; Knowledge management ; Optimization ; Scalability
Indexed Type: EI
ISBN: 9781450311564
Department: (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
Sponsorship: Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEB
Abstract: 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.
English Abstract: 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15888
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Qin Xiulei,Zhang Wenbo,Wang Wei,et al. optimizing data migration for cloud-based key-value stores[C]. 见:21st ACM International Conference on Information and Knowledge Management, CIKM 2012. Maui, HI, United states. October 29, 2012 - November 2, 2012.
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