| Subject: | Computer Science
|
| Title: | on-line cache strategy reconfiguration for elastic caching platform: a machine learning approach |
| Author: | Qin Xiulei
; Zhang Wenbo
; Wang Wei
; Wei Jun
; Zhong Hua
; Huang Tao
|
| Source: | Proceedings - International Computer Software and Applications Conference
|
| Conference Name: | 35th Annual IEEE International Computer Software and Applications Conference, COMPSAC 2011
|
| Conference Date: | July 18, 2
|
| Issued Date: | 2011
|
| Conference Place: | Munich, Germany
|
| Keyword: | Cloud computing
; Fault tolerance
; Learning systems
; Scalability
; User interfaces
|
| Indexed Type: | EI
; ISTP
|
| ISSN: | 0730-3157
|
| ISBN: | 9780769544397
|
| Department: | (1) Institute of Software Chinese Academy of Sciences China; (2) State Key Laboratory of Computer Science China; (3) Graduate University of Chinese Academy of Sciences Beijing China
|
| Sponsorship: | IEEE; IEEE Computer Society
|
| Abstract: | 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. |
| English Abstract: | 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. |
| Language: | 英语
|
| Content Type: | 会议论文
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| URI: | http://ir.iscas.ac.cn/handle/311060/16201
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| 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. 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.
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