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Prediction-Based VM Provisioning and Admission Control for Multi-Tier Web Applications

Adnan Ashraf, Benjamin Byholm, Ivan Porres, Prediction-Based VM Provisioning and Admission Control for Multi-Tier Web Applications. Journal of Cloud Computing 5(1), 1–21, 2016.

http://dx.doi.org/10.1186/s13677-016-0065-9

Abstract:

We present a prediction-based, cost-efficient Virtual Machine (VM) provisioning and admission control approach for multi-tier web applications. The proposed approach provides automatic deployment and scaling of multiple web applications on a given Infrastructure as a Service (IaaS) cloud. It monitors and uses collected resource utilization metrics itself and does not require a performance model of the applications or the infrastructure dynamics. The approach uses the OSGi component model to share VM resources among deployed applications, reducing the total number of required VMs. The proposed approach comprises three sub-approaches: a reactive VM provisioning approach called ARVUE, a hybrid reactive-proactive VM provisioning approach called Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling (CRAMP), and a session-based adaptive admission control approach called adaptive Admission Control for Virtualized Application Servers (ACVAS). Performance under varying load conditions is guaranteed by automatic adjustment and tuning of the CRAMP and ACVAS parameters. The proposed approach is demonstrated in discrete-event simulations and is evaluated in a series of experiments involving synthetic as well as realistic load patterns.

BibTeX entry:

@ARTICLE{jAsByPo16a,
  title = {Prediction-Based VM Provisioning and Admission Control for Multi-Tier Web Applications},
  author = {Ashraf, Adnan and Byholm, Benjamin and Porres, Ivan},
  journal = {Journal of Cloud Computing},
  volume = {5},
  number = {1},
  publisher = {SpringerOpen},
  pages = {1–21},
  year = {2016},
  keywords = {Cloud computing, Virtual machine provisioning, Admission control, Web application, Cost-efficiency, Performance},
  ISSN = {2192-113X},
}

Belongs to TUCS Research Unit(s): Software Engineering Laboratory (SE Lab)

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