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| optimizing data migration for cloud-based key-value stores | |
| Qin Xiulei; Zhang Wenbo; Wang Wei; Wei Jun; Zhao Xin; Huang Tao | |
| 2012 | |
| Conference Name | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 |
| Source | ACM International Conference Proceeding Series |
| Pages | 2204-2208 |
| Conference Date | October 29, 2012 - November 2, 2012 |
| Conference Place | Maui, HI, United states |
| 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 |
| 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.; 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. |
| Keyword | Elasticity Knowledge Management Optimization Scalability |
| Sponsorship | Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEB |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15888 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Qin Xiulei,Zhang Wenbo,Wang Wei,et al. optimizing data migration for cloud-based key-value stores[C],2012:2204-2208. |
| Files in This Item: | There are no files associated with this item. | |||||
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