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CloudBench: an integrated evaluation of VM placement algorithms in clouds

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Abstract

A complex and important task in the cloud resource management is the efficient allocation of virtual machines (VMs), or containers, in physical machines (PMs). The evaluation of VM placement techniques in real-world clouds can be tedious, complex and time-consuming. This situation has motivated an increasing use of cloud simulators that facilitate this type of evaluations. However, most of the reported VM placement techniques based on simulations have been evaluated taking into account one specific cloud resource (e.g., CPU), whereas values often unrealistic are assumed for other resources (e.g., RAM, awaiting times, application workloads, etc.). This situation generates uncertainty, discouraging their implementations in real-world clouds. This paper introduces CloudBench, a methodology to facilitate the evaluation and deployment of VM placement strategies in private clouds. CloudBench considers the integration of a cloud simulator with a real-world private cloud. Two main tools were developed to support this methodology, a specialized multi-resource cloud simulator (CloudBalanSim), which is in charge of evaluating VM placement techniques, and a distributed resource manager (Balancer), which deploys and tests in a real-world private cloud the best VM placement configurations that satisfied user requirements defined in the simulator. Both tools generate feedback information, from the evaluation scenarios and their obtained results, which is used as a learning asset to carry out intelligent and faster evaluations. The experiments implemented with the CloudBench methodology showed encouraging results as a new strategy to evaluate and deploy VM placement algorithms in the cloud.

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Notes

  1. The DCSim code is public and can be downloaded from: https://github.com/digs-uwo/dcsim.

  2. FlexCloud: https://sourceforge.net/projects/flexcloud/.

  3. http://monasca.io/about.html, https://wiki.openstack.org/wiki/Monasca.

  4. https://www.openstack.org.

  5. https://github.com/beloglazov/planetlab-workload-traces.

  6. https://docs.ceph.com/docs/master/start/intro/.

  7. https://zookeeper.apache.org/doc/current/zookeeperOver.html.

  8. https://cloud.google.com/kubernetes/.

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Acknowledgements

This work was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness under the Grant TIN2016-79637-P “Towards Unification of HPC and Big Data Paradigms” and by the Mexican Council of Science and Technology (CONACYT) through a Ph.D. Grant (No. 212677).

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Authors and Affiliations

  1. CINVESTAV Unidad Tamaulipas, Ciudad Victoria, Mexico

    Mario A. Gomez-Rodriguez & Jose Luis Gonzalez

  2. Universidad Carlos III de Madrid, Madrid, Spain

    Victor J. Sosa-Sosa & Jesus Carretero

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  1. Mario A. Gomez-Rodriguez
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  2. Victor J. Sosa-Sosa
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Correspondence to Mario A. Gomez-Rodriguez.

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Gomez-Rodriguez, M.A., Sosa-Sosa, V.J., Carretero, J. et al. CloudBench: an integrated evaluation of VM placement algorithms in clouds. J Supercomput 76, 7047–7080 (2020). https://doi.org/10.1007/s11227-019-03141-9

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