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Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

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Abstract

Growing demand for reduced local hardware infrastructure is driving the adoption of Cloud Computing. In the Infrastructure-as-a-Service model, service providers offer virtualized computational resources in the form of virtual machine instances. The existence of a large variety of providers and instances makes the decision-making process a difficult task for users, especially as factors such as the datacenter location - where the virtual machine is hosted - have a direct influence on the price of instances. The same instance may present price differences when hosted in different geographically distributed datacenters and, because of that, the datacenter location needs to be taken into account through the decision-making process. Given this problem, we propose the D-AHP, a methodology to aid decision-making based on Pareto Dominance and Analytic Hierarchy Process (AHP). In the D-AHP, the dominance concept is applied to reduce the number of instances to be compared; the instances selection is based on a set of objectives, while AHP ranks the selected ones from a set of criteria and sub-criteria, among them the datacenter location. The results from case studies show that differences may arise in the results, regarding which instance is more suitable for the user, when considering the datacenter location as a criterion to choose an instance. This fact highlights the need to consider this factor during the process of migrating applications to the Cloud. In addition, Pareto Dominance applied early over the set of total instances has proved to be efficient, once it significantly reduces the number of instances to be compared and ordered by the AHP by excluding instances with less computational resources and higher cost in the decision-making process, mainly for larger application workloads.

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Acknowledgements

The research work on which we report in this paper is supported the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS) and the internal Research Programme at UNIJUI University. First author is also thanks the UFFS University for the support to the development of his research.

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  1. Federal University of Fronteira Sul, Av. Jacob Reinaldo Haupenthal, s/n, Cerro Largo, 97900-000, Brazil

    Cássio L. M. Belusso

  2. UNIJUI University, Rua do Comércio, 3000, Ijuí, 98700-000, Brazil

    Sandro Sawicki, Rafael Z. Frantz & Fabricia Roos-Frantz

  3. University Institute of Lisbon (ISTAR-IUL), Av. das Forças Armadas, 376, 1649-026, Lisbon, Portugal

    Vitor Basto-Fernandes

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  1. Cássio L. M. Belusso
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Appendix A: additional tables referring to the case studies

Appendix A: additional tables referring to the case studies

See Tables 19, 20, 21, 22, 23, and 24.

Table 19 Results of pairwise comparison between the criteria for each DM
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Table 20 Results of the pairwise comparison between sub-criteria of the Computational Resources criterion for each DM
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Table 21 Valuation of instances in relation to the decision criteria
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Table 22 Valuation of instances in relation to the decision criteria
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Table 23 Final classification of instances for each DM
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Table 24 Final classification of instances for each DM
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Belusso, C.L.M., Sawicki, S., Basto-Fernandes, V. et al. Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context. J Supercomput 78, 7825–7860 (2022). https://doi.org/10.1007/s11227-021-04248-8

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