Analytical-stochastic availability model of multi-functional cloud infrastructure

Authors

  • V. Don Department of Applied Mathematics and Information Technologies. Pridniprovsk State Academy of Cyvil Engineering and Architecture. 24-a Chernishevskogo st. 49600, Dnipropetrovsk
  • O. Shibko Department of Applied Mathematics and Information Technologies. Pridniprovsk State Academy of Cyvil Engineering and Architecture. 24-a Chernishevskogo st. 49600, Dnipropetrovsk

DOI:

https://doi.org/10.30838/P.CMM.2415.270818.20.224

Keywords:

cloud computing, semi-Markov chains, cloud infrastructure, model, availability

Abstract

Purpose. Analyze stochastic methods for metamodeling of cloud infrastructures (СI) based on conceptual requirements for ensuring their readiness taking into account performance requirements, scalability of the computing resource, energy saving and elasticity of control of the components of the СI. The technique. The analysis of stochastic methods used to model the behavior and assess readiness indicators, the reliability of cloud infrastructures (СI) is performed. As a fundamental basis, the taxonomy of OO metamodeling is considered, which is based on the conceptual foundations of ensuring productivity, resource scalability, energy saving, and elasticity of management of the components of the cloud infrastructure. In addition to the well-known estimation methods using the apparatus of stochastic Petri nets, Markov chains are proposed to focus on the possibility of using semi-Markov modeling methods that contribute to improving the accuracy of evaluating the metrics of the quality of services provided (QoS). Results. The proposed taxonomy is a fundamental basis for modeling behavior and assessing the availability and reliability of cloud infrastructures. Scientific novelty. The problem of the lack of methodology for modeling the processes of СI functioning is solved, which is based on a single taxonomic basis of a comprehensive solution to the problem of assessing, analyzing and monitoring the level of readiness of cloud infrastructures. Practical value. The analysis opens up the possibility of using stochastic methods to ensure performance, resource scalability, energy saving and elasticity of management of the components of the cloud infrastructure. The considered methods of modeling can be applied to the selection of optimal architectural solutions in accordance with the established criterion of readiness of cloud infrastructures.

Author Biographies

V. Don, Department of Applied Mathematics and Information Technologies. Pridniprovsk State Academy of Cyvil Engineering and Architecture. 24-a Chernishevskogo st. 49600, Dnipropetrovsk

student

O. Shibko, Department of Applied Mathematics and Information Technologies. Pridniprovsk State Academy of Cyvil Engineering and Architecture. 24-a Chernishevskogo st. 49600, Dnipropetrovsk

P.G., Associate Prof.

References

Ivanchenko O.V Obobshchennyy kriteriy upravleniya konfiguratsiyey infrastruktury kak servisa oblachnykh vychisleniy [Generalized Criterion for Configuring Infrastructure as a Cloud Computing Service]. Header of UMSF. – 2013, no. 1, pp. 2-14. (in Russian)

Ivanchenko O.V. Polumarkovskaya model nadezhnosti infrastruktury kak servisa oblachnykh vychisleniy [The semi-Markov model of reliability of infrastructure as a cloud computing service]. Visnyk AMSU, 2014, no. 6, pp. 3-15. (in Russian)

Infrastructure security and reliability. Available at: http:// www. rackspace. co.uk/ security-reliability).

Cloud Computing Takes Off. Available at: http:// www.morganstanley. com/ views/ perspectives/cloud_computing.pdf .

Cloud Computing with Amazon Web Services. Available at: https://aws.amazon.com .

Ghosh R. Stochastic Model Driven Capacity Planning for an Infrastructure-as-a-Service Cloud. IEEE Transactions On Services Computing, vol. 7, no. 4, 2013, pp. 667 – 680.

Ghosh R. End-to-End Performability Analysis for Infrastructure-as-a-Service Cloud, Available at: https://www.researchgate.net/profile/Kishor_Trivedi2.

Khazaei H. Availability analysis of cloud computing centers. Available at: http://www.scs.ryerson.ca/~jmisic/papers/gc12hamzeh.pdf (in English).

Published

2018-11-27

Issue

Section

Computer systems and information technologies in education, science and management