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Optimising QoS-Assurance, Resource Usage and Cost of Fog Application Deployments

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Book cover Cloud Computing and Services Science (CLOSER 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1073))

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Abstract

Identifying the best application deployment to distribute application components in Fog infrastructures – spanning the IoT-to-Cloud continuum – is a challenging task for application deployers. Indeed, it requires fulfilling all application requirements, whilst determining a trade-off among different objectives (i.e., QoS assurance, Fog resource consumption and cost), resulting in a complex and time-consuming decision-making process to be tuned manually. In this paper, we present a simple multi-objective optimisation scheme that permits selecting the best placement of application components, balancing the trade-off among QoS-assurance, Fog resource consumption and monthly deployment costs. We exploit our prototype, extended with parallel Monte Carlo simulations, and a motivating example to show how IT experts can benefit from our approach.

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Notes

  1. 1.

    Hereinafter, the word Things is used to refer to IoT devices, both sensors and actuators.

  2. 2.

    Adapted from OpenStack Mitaka flavours: https://docs.openstack.org/.

  3. 3.

    Arrows on the links in Fig. 1 indicate the upload direction.

  4. 4.

    Arrows on the links in Fig. 2 indicate the upload direction.

  5. 5.

    Satellite: https://www.eolo.it/, 3G/4G: https://www.agcom.it, VDSL: http://www.vodafone.it.

  6. 6.

    Available at https://github.com/di-unipi-socc/FogTorchPI/tree/multithreaded/.

  7. 7.

    Actual implementations in Fog landscapes can rely on monitoring tools (e.g., [6, 22]) to update the information available on I.

  8. 8.

    When \(\updelta \) is not specified for a component \(\upgamma \) of A, \(\upgamma \) can be deployed to any compatible node in I.

  9. 9.

    \(\textsf {FogTorch}\Pi \) permits to compute Fog resource consumption also on a specified subset of Fog nodes \(\overline{F} \subset F\).

  10. 10.

    Cost computation is performed on-the-fly. This is done during the search step, considering the possibility to rely on the cost prediction as a heuristic to lead backtracking towards best candidate deployments.

  11. 11.

    Bounded by the maximum amount purchasable from any chosen Cloud or Fog provider.

  12. 12.

    €30 = 1 CPU \(\times \) €4/core + 1 GB RAM \(\times \) €6/GB + 20 GB HDD \(\times \) €1/GB.

  13. 13.

    Other policies are also possible such as, for instance, selecting the largest offering that can accommodate a component, or always increasing the component’s requirements by some percentage (e.g., \(10\%\)) before selecting the matching.

  14. 14.

    For the sake of simplicity, we assume here \(\upomega _i = \frac{1}{|F|} = \frac{1}{m}\), which can be tuned differently depending on the needs of the application operator.

  15. 15.

    Results and Python code to generate 3D plots as in Figs. 4, 5 and 6 are available at: https://github.com/di-unipi-socc/FogTorchPI/tree/costmodel/results/SMARTBUILDING18/.

  16. 16.

    By tuning \(\upomega _i\) differently and by considering the Cloud-ward case, we can obtain the same results of [9], e.g. assigning weight 0.50 to both resource consumption and cost, and 0.0 to QoS-assurance \(\Delta 2\) is ranked 0.34 whilst \(\Delta 7\) scores 0.22.

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Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Pisa, Italy

    Antonio Brogi & Stefano Forti

  2. School of Computer Science, University of Birmingham, Birmingham, UK

    Ahmad Ibrahim

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  1. Antonio Brogi
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  2. Stefano Forti
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Corresponding author

Correspondence to Stefano Forti .

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Editors and Affiliations

  1. Escola d’Enginyeria, Bellaterra, Barcelona, Spain

    Víctor Méndez Muñoz

  2. Columbia University, New York, USA

    Donald Ferguson

  3. Dublin City University, Dublin 9, Ireland

    Markus Helfert

  4. Free University of Bozen-Bolzano, Bolzano, Bolzano, Italy

    Claus Pahl

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Brogi, A., Forti, S., Ibrahim, A. (2019). Optimising QoS-Assurance, Resource Usage and Cost of Fog Application Deployments. In: Muñoz, V., Ferguson, D., Helfert, M., Pahl, C. (eds) Cloud Computing and Services Science. CLOSER 2018. Communications in Computer and Information Science, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-29193-8_9

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  • DOI: https://doi.org/10.1007/978-3-030-29193-8_9

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