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Markov Prediction Model for Host Load Detection and VM Placement in Live Migration


Markov Prediction Model for Host Load Detection and VM Placement in Live Migration

Melhem, Suhib Bani ORCID: https://orcid.org/0000-0003-4998-3418, Agarwal, Anjali, Goel, Nishith and Zaman, Marzia (2018) Markov Prediction Model for Host Load Detection and VM Placement in Live Migration. IEEE Access, 6 . pp. 7190-7205. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2017.2785280


The design of good host overload/underload detection and virtual machine (VM) placement algorithms plays a vital role in assuring the smoothness of VM live migration. The presence of the dynamic environment that leads to a changing load on the VMs motivates us to propose a Markov prediction model to forecast the future load state of the host. We propose a host load detection algorithm to find the future overutilized/underutilized hosts state to avoid immediate VMs migration. Moreover, we propose a VM placement algorithm to determine the set of candidates hosts to receive the migrated VMs in a way to reduce their VM migrations in near future. We evaluate our proposed algorithms through CloudSim simulation on different types of PlanetLab real and random workloads. The experimental results show that our proposed algorithms have a significant reduction in terms of service-level agreement violation, the number of VM migrations, and other metrics than the other competitive algorithms.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Authors:Melhem, Suhib Bani and Agarwal, Anjali and Goel, Nishith and Zaman, Marzia
Journal or Publication:IEEE Access
  • Concordia Open Access Author Fund
Digital Object Identifier (DOI):10.1109/ACCESS.2017.2785280
Keywords:VM live migration, host overload/underload detection, VM placement, CloudSim
ID Code:983716
Deposited On:10 Apr 2018 20:08
Last Modified:10 Apr 2018 20:08


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