on-line cache strategy reconfiguration for elastic caching platform: a machine learning approach
文献类型：会议论文
| 作者 | Qin Xiulei ; Zhang Wenbo ; Wang Wei ; Wei Jun ; Zhong Hua ; Huang Tao |
| 出版日期 | 2011 |
| 会议名称 | 35th Annual IEEE International Computer Software and Applications Conference, COMPSAC 2011 |
| 会议日期 | July 18, 2 |
| 会议地点 | Munich, Germany |
| 关键词 | Cloud computing Fault tolerance Learning systems Scalability User interfaces |
| 页码 | 523-534 |
| 中文摘要 | Cloud computing provide scalability and high availability for web applications using such techniques as distributed caching and clustering. As one database offloading strategy, elastic caching platforms (ECPs) are introduced to speed up the performance or handle application state management with fault tolerance. Several cache strtegies for ECPs have been proposed, say replicated strategy, partitioned strategy and near strategy. We first evaluate the impact of the three cache strategies using the TPC-W benchmark and find that there is no single cache strategy suitable for all conditions, the selection of the best strategy is related with workload patterns, cluster size and the number of concurrent users. This raises the question of when and how the cache strategy should be reconfigured as the condition varies which has received comparatively less attention. In this paper, we present a machine learning based approach to solving this problem. The key features of the approach are off-line training coupled with on-line system monitoring and robust synchronization process after triggering a reconfiguration, at the same time the performance model is periodically updated. More explicitly, first a rule set used to identify which cache strategy is optimal under the current condition are trained with the system statistics and performance results. We then introduce a framework to switch the cache strategy on-line as the workload varies and keep its overhead to acceptable levels. Finally, we illustrate the advantages of this approach by carrying out a set of experiments. © 2011 IEEE. |
| 英文摘要 | Cloud computing provide scalability and high availability for web applications using such techniques as distributed caching and clustering. As one database offloading strategy, elastic caching platforms (ECPs) are introduced to speed up the performance or handle application state management with fault tolerance. Several cache strtegies for ECPs have been proposed, say replicated strategy, partitioned strategy and near strategy. We first evaluate the impact of the three cache strategies using the TPC-W benchmark and find that there is no single cache strategy suitable for all conditions, the selection of the best strategy is related with workload patterns, cluster size and the number of concurrent users. This raises the question of when and how the cache strategy should be reconfigured as the condition varies which has received comparatively less attention. In this paper, we present a machine learning based approach to solving this problem. The key features of the approach are off-line training coupled with on-line system monitoring and robust synchronization process after triggering a reconfiguration, at the same time the performance model is periodically updated. More explicitly, first a rule set used to identify which cache strategy is optimal under the current condition are trained with the system statistics and performance results. We then introduce a framework to switch the cache strategy on-line as the workload varies and keep its overhead to acceptable levels. Finally, we illustrate the advantages of this approach by carrying out a set of experiments. © 2011 IEEE. |
| 收录类别 | EI ; ISTP |
| 会议主办者 | IEEE; IEEE Computer Society |
| 会议录 | Proceedings - International Computer Software and Applications Conference
 |
| 学科主题 | Computer Science |
| 语种 | 英语 |
| ISSN号 | 0730-3157 |
| ISBN号 | 9780769544397 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16201]  |
| 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. on-line cache strategy reconfiguration for elastic caching platform: a machine learning approach[C]. 见:35th Annual IEEE International Computer Software and Applications Conference, COMPSAC 2011. Munich, Germany. July 18, 2. |
入库方式： OAI收割
来源：软件研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。