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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.

Because of highly distributed nature of software products, the task of improving software reliability is becoming a complex job. Specialized tools and techniques are being used to isolate the risky software modules. In order to retain in market, firms are required not only to provide the software on time but also to endow with continuous processing. Product updating is the process that comes to safeguard the firm's image at this point of time. And one such attribute of updating is providing software patches. Today, almost all software firms are providing either patching, module exchange or service pack application processes consequent to a release. This paper proposes a scheduling policy for a software product and shows the importance of patching in lowering the system outages and making the system more cost effective. Validation on the proposed policy has been done using real life software failure data set of Tandem Computers. Copyright © 2016 John Wiley & Sons, Ltd.

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Software development environment has been changing into new development paradigms such as concurrent distributed development environment and the so-called open source project by using network computing technologies. Especially, an OSS (open source software) system which serves as key components of critical infrastructures in the society is still ever-expanding now. In case of considering the effect of the debugging process on an entire system in the development of a method of reliability assessment for the OSS, it is necessary to grasp the deeply-intertwined factors, such as programming path, size of each component, skill of fault reporter, and so on. In order to consider the effect of each software component on the reliability of an entire system, we propose a new approach to user-oriented software reliability assessment by creating a fusion of neural network and software reliability growth modeling. In this paper, we show application examples of component-oriented software reliability assessment based on neural network and software reliability growth modeling for the OSS. Also, we analyze actual software fault count data to show numerical examples of software reliability assessment for the OSS. Moreover, we develop the testing management tool for OSS.