期刊
JOURNAL OF GRID COMPUTING
卷 12, 期 4, 页码 559-592出版社
SPRINGER
DOI: 10.1007/s10723-014-9314-7
关键词
Cloud computing; Scalable applications; Auto-scaling; Service level agreement
资金
- Basque Government [IT-242-07]
- COM-BIOMED network in computational biomedicine (Carlos III Health Institute)
- [TIN2013-41272P]
Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.
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