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Journal of Grid Computing

, Volume 12, Issue 4, pp 559–592 | Cite as

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

  • Tania Lorido-Botran
  • Jose Miguel-Alonso
  • Jose A. Lozano
Article

Abstract

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.

Keywords

Cloud computing Scalable applications Auto-scaling Service level agreement 

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Tania Lorido-Botran
    • 1
    • 2
  • Jose Miguel-Alonso
    • 1
    • 2
  • Jose A. Lozano
    • 1
    • 3
  1. 1.Intelligent Systems GroupUniversity of the Basque Country, UPV/EHUDonostia-San SebastiánSpain
  2. 2.Department of Computer Architecture and Technology Donostia-San SebastianSpain
  3. 3.Department of Computer Science and Artificial IntelligenceDonostiaSpain

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