International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749

Dependability Analysis Tool Based on Multi-Dimensional Stochastic Noisy Model for Cloud Computing with Big Data

Yoshinobu Tamura
Tokyo City University, Tamazutsumi 1-28-1, Setagaya-ku, Tokyo 158-8557, Japan.

Shigeru Yamada
Tottori University, Minami 4-101, Koyama, Tottori-shi, 680-8552, Japan.

DOI https://dx.doi.org/10.33889/IJMEMS.2017.2.4-021

Received on November 20, 2016
  ;
Accepted on March 03, 2017

Abstract

This paper focuses on a big data on cloud computing environment by using open source software such as Open Stack and Eucalyptus because of the unification management of data and low cost. We propose a new approach to software dependability assessment based on stochastic differential equation modelling and jump diffusion process modelling in order to consider the interesting aspect of the numbers of components, cloud applications, and users. Moreover, we discuss the determination of an optimum software maintenance time minimizing the total expected software cost. In particular, we develop the three-dimensional AIR application for reliability and cost optimization analysis based on the proposed method. Then, we show numerical performance of the developed AIR application to evaluate the method of software reliability assessment for the big data on cloud computing.

Keywords- Cloud computing, Reliability modelling, Jump diffusion process, Cost optimization.

Citation

Tamura, Y., & Yamada, S. (2017). Dependability Analysis Tool Based on Multi-Dimensional Stochastic Noisy Model for Cloud Computing with Big Data. International Journal of Mathematical, Engineering and Management Sciences, 2(4), 273-287. https://dx.doi.org/10.33889/IJMEMS.2017.2.4-021.

Conflict of Interest

Acknowledgements

This work was supported in part by the Telecommunications Advancement Foundation in Japan, the Okawa Foundation for Information and Telecommunications in Japan, and the JSPS KAKENHI Grant No. 15K00102 and No. 16K01242 in Japan.

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