IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Reliability Modeling and Assessment for Open Source Cloud Software: A Stochastic Approach

Reliability Modeling and Assessment for Open Source Cloud Software: A Stochastic Approach
View Sample PDF
Author(s): Yoshinobu Tamura (Yamaguchi University, Japan)and Shigeru Yamada (Tottori University, Japan)
Copyright: 2015
Pages: 22
Source title: Open Source Technology: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-7230-7.ch052

Purchase

View Reliability Modeling and Assessment for Open Source Cloud Software: A Stochastic Approach on the publisher's website for pricing and purchasing information.

Abstract

Software development based on the Open Source Software (OSS) model is being increasingly accepted to stand up servers and applications. In particular, Cloud OSS is now attracting attention as the next generation of software products due to cost efficiencies and quick delivery. This chapter focuses on the software reliability modeling and assessment for Cloud computing infrastructure software, especially open source software, such as OpenStack and Eucalyptus. In this chapter, the authors introduce a new approach to the Jump diffusion process based on stochastic differential equations in order to consider the interesting aspect of the numbers of components and users in the reliability model. In addition, the authors consider the network traffic of the Cloud in the reliability modeling and integrate the reliability model with a threshold-based neural network approach that estimates network traffic. Actual software fault-count data are analyzed in order to show numerical examples of software reliability assessment. This chapter also illustrates how the proposed method of reliability analysis can assist in quality improvement in Cloud computing software.

Related Content

Karl-Michael Popp. © 2023. 17 pages.
Marco Berlinguer. © 2023. 32 pages.
Laetitia Marie Thomas, Karine Evrard-Samuel, Peter Troxler. © 2023. 30 pages.
RenĂª de Souza Pinto. © 2023. 48 pages.
Francisco Jose Monaco. © 2023. 47 pages.
Marcelo Schmitt, Paulo Meirelles. © 2023. 25 pages.
Hillary Nyakundi, Cesar Henrique De Souza. © 2023. 39 pages.
Body Bottom