optimizing data migration for cloud-based key-value stores
文献类型：会议论文
| 作者 | Qin Xiulei ; Zhang Wenbo ; Wang Wei ; Wei Jun ; Zhao Xin ; Huang Tao |
| 出版日期 | 2012 |
| 会议名称 | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 |
| 会议日期 | October 29, 2012 - November 2, 2012 |
| 会议地点 | Maui, HI, United states |
| 关键词 | Elasticity Knowledge management Optimization Scalability |
| 页码 | 2204-2208 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | Special Interest Group on Information Retrieval (ACM SIGIR); ACM SIGWEB |
| 会议录 | ACM International Conference Proceeding Series
 |
| 语种 | 英语 |
| ISBN号 | 9781450311564 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15888]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 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]. 见:21st ACM International Conference on Information and Knowledge Management, CIKM 2012. Maui, HI, United states. October 29, 2012 - November 2, 2012. |
入库方式： OAI收割
来源：软件研究所
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