- Title
- Predicting Hot-Spots in distributed cloud databases using association rule mining
- Creator
- Kaml, Joarder; Murshed, Manzur; Gaber, Mohamed
- Date
- 2014
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161666
- Identifier
- vital:12528
- Identifier
-
https://doi.org/10.1109/UCC.2014.130
- Identifier
- ISBN:978-1-4799-7881-6
- Abstract
- Data partitioning is a popular technique to horizontally or vertically split table attributes of a Cloud database cluster to evenly distribute increasing workloads. However, hot-spots can be created due to inappropriate partitioning scheme and static partition management without considering the dynamic workload characteristics. In this paper, an automatic database partition management scheme - APM - is proposed which periodically analyses workload logs to predict the formation of any potential hot-spot using association rule mining. A detailed illustration of the proposed scheme is presented with examples along with a cost model following by experimental observations from running a HBase cluster with YCSB workloads in AWS.
- Publisher
- IEEE
- Relation
- IEEE/ACM 7th International Conference Utility and Cloud Computing (UCC), 2014; London; 8-11th December, 2014 p. 800-805
- Rights
- Copyright
- Rights
- This metadata is freely available under a CCO license
- Subject
- Servers; Databases; Analytical models; Data mining
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