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A prediction-based model for virtual machine live migration monitoring in a cloud datacenter

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Abstract

Live migration of virtual machines proves to be inexorable in providing load balancing among physical devices and allowing scalability and flexibility in resource allocation. The existing approaches exhibit different policies, distinct performance characteristics, and side effects such as power consumption and performance degradation. Therefore, determining the most optimal live migration algorithm in certain situations remains an open challenge. In this work, a new prediction-based model to manage the live migration process of VMs is introduced. Our adaptive model dynamically identifies the optimal live migration algorithm for a given performance metric based on a prior diagnosis of the system. The model is developed by considering the assumption of different workloads alongside certain resource constraints for any of the currently available migration algorithms. The proposed model consists of an ensemble-learning strategy that involves linear and non-parametric regression methods to predict six live migration key metrics, provided by the operator and/or the user, for each live migration algorithm. Our model allows considering the best combination which is constituted of the algorithm-metric pair to migrate a VM. The experimental results show that the proposed model allows to significantly alleviate the service level agreement violation rate by between \(31\%\) and \(60\%\), along with decreasing the total CPU time required for the prediction process.

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Data availability

The data that support the findings of this paper are available from the corresponding author, Saloua El Motaki, upon reasonable request.

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Acknowledgements

This research was supported through computational resources of HPC-MARWAN (hpc.marwan.ma) provided by the National Center for Scientific and Technical Research (CNRST), Rabat, Morocco.

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The authors of this paper have not received any financial support for research, authorship and/or publication of this article.

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Authors and Affiliations

  1. University Sidi Mohamed Ben Abdellah, Fez, Morocco

    Saloua El Motaki & Ali Yahyaouy

  2. University of Caen Normandy, Caen, France

    Hamid Gualous

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  1. Saloua El Motaki
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  2. Ali Yahyaouy
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  3. Hamid Gualous
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Contributions

Conceptualization: SEM and AY; Formal analysis and implementation: SEM and AY; Writing - original draft preparation: Saloua El Motaki; Writing - review and editing: SEM, AY and HG; Supervision: AY.

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Correspondence to Saloua El Motaki.

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Motaki, S.E., Yahyaouy, A. & Gualous, H. A prediction-based model for virtual machine live migration monitoring in a cloud datacenter. Computing 103, 2711–2735 (2021). https://doi.org/10.1007/s00607-021-00981-3

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