Abstract
At present, many cloud services are managed by using open source software, such as OpenStack and Eucalyptus, because of the unification management of data, cost reduction, quick delivery and work savings. The operation phase of cloud computing has a unique feature, such as the provisioning processes, the network-based operation and the diversity of data, because the operation phase of cloud computing changes depending on many external factors. We propose a jump diffusion model with two-dimensional Wiener processes in order to consider the interesting aspects of the network traffic and big data on cloud computing. In particular, we assess the stability of cloud software by using the sample paths obtained from the jump diffusion model with two-dimensional Wiener processes. Moreover, we discuss the optimal maintenance problem based on the proposed jump diffusion model. Furthermore, we analyze actual data to show numerical examples of dependability optimization based on the software maintenance cost considering big data on cloud computing.
Highlights
At present, big data and cloud computing are attracting attention as the next-generation software service paradigm
We have discussed a software dependability assessment based on the jump diffusion model with a two-dimensional Wiener processes in order to consider the software management environment of big data on cloud computing
We have assumed that several factors, big data, cloud computing and network access, have an effect on cloud software, indirectly
Summary
Big data and cloud computing are attracting attention as the next-generation software service paradigm. OSS (open source software) systems serve as key components of critical infrastructures in society. It is difficult for many companies to assess the reliability in mobile clouds, because a mobile OSS includes several software versions, vulnerability issues, open source code, security holes, etc. It is important for software managers to consider these external factors for big data, mobile clouds and cloud computing. We propose a method of software reliability analysis based on a jump diffusion model with a stochastic differential equation for big data on cloud computing. We show that the proposed reliability and optimization analysis can assist with the improvement of the quality for big data on a cloud computing environment
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