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A regression tree predictive model for virtual machine startup time in IaaS clouds

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Abstract

Cloud computing provides effective ways to rapidly provision computing resources over the Internet. For a better management of resource provisioning, the system requires to predict service-level agreements (SLAs) such as virtual machine (VM) startup times under various conditions of computing resources. The VM startup time is an important SLA parameter, which can impact other SLA parameters such as service initiation time and VM scale out times. By predicting VM startup times, Cloud providers can improve Cloud users’ expectations. Various quality of service (QoS) parameters have been considered in different resource allocation frameworks. Also, there are several efforts addressing QoS prediction in cloud environments. However, little research has considered VM startup time as a QoS parameter. In this paper, we propose a regression tree model for predicting average, minimum, and maximum VM startup times. To test the efficiency of our model, we implemented the model in an OpenStack test environment. The test results show that our model predicts VM startup times with an average accuracy of 91.81%.

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Notes

  1. Scale_up time is the time taken to increase a specific number of VMs.

  2. Scale_down time is the time taken to decrease a specific number of VMs.

  3. Overfitting leads to high noise interference. i.e. the model tries to incorporate noise in the training data to the learning phase and, as a consequence, reduces the prediction accuracy.

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Acknowledgements

Yatheendraprakash Govindaraju would like to thank the Mexican National Council for Science and Technology (CONACyT) for the full-time scholarship of his postgraduate studies.

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Authors and Affiliations

  1. Department of Information Systems, Centro Universitario de Ciencias Economico Administrativas (CUCEA), University of Guadalajara, Guadalajara, Mexico

    Yatheendraprakash Govindaraju & Hector A. Duran-Limon

  2. Artificial Intelligence Research Center, University of Veracruz, Xalapa, Mexico

    Efrén Mezura-Montes

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  1. Yatheendraprakash Govindaraju
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Correspondence to Yatheendraprakash Govindaraju.

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Govindaraju, Y., Duran-Limon, H.A. & Mezura-Montes, E. A regression tree predictive model for virtual machine startup time in IaaS clouds. Cluster Comput 24, 1217–1233 (2021). https://doi.org/10.1007/s10586-020-03169-0

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