#Boinc climate prediction software
#Boinc climate prediction series

We hold a series of interviews with environmental scientists involved in developing and deploying computer based environmental models about the approach taken in engineering models, and describe a case study in deploying an environmental model (WRF: Weather Research Forecasting) on a cloud architecture.

#Boinc climate prediction software

In this paper we aim to capture the software engineering practices of environmental scientists, highlight opportunities for software engineering and work towards developing a domain specific language for the configuration and deployment of environmental models. New and emergent computing architectures and software engineering practices provide an opportunity for environmental models to be deployed more efficiently and democratically. We also discuss problems related to computing security, reliability and scientific reproducibility. Finally, we discuss how cloud computing can be used for geoscientific modelling, including issues related to the allocation of resources by funding bodies. The computational performance and cost of each model within this new type of environment are discussed, and an assessment is given in qualitative terms. The adaptations and procedures necessary to run the models in these environments are described. Each was customized in a different way to run in public cloud computing environments (hereafter cloud computing) provided by three different public vendors: Amazon, Google and Microsoft. In this paper, we discuss the use of cloud computing as a tool to improve the range of resources available for climate science, presenting the evaluation of two different climate models.

We show how this design can be used as an efficient distributed computing platform within the cloud, and outline new approaches that could open up new possibilities in this field, using () as a case study.Ĭloud computing is a mature technology that has already shown benefits for a wide range of academic research domains that, in turn, utilize a wide range of application design models. This is difficult to achieve using volunteers, and at the same time, using scalable cloud resources for short on demand projects can optimize the use of the available resources. We discuss the way in which cloud services can help BOINC-based projects to deliver results in a fast, on demand manner. The BOINC system is ideal in those cases where not only does the research community involved need low-cost access to massive computing resources but also where there is a significant public interest in the research being done. The majority of these have been built using the Berkeley Open Infrastructure for Network Computing (BOINC) platform, which provides a range of different services to manage all computation aspects of a project. Volunteer or crowd computing is becoming increasingly popular for solving complex research problems from an increasingly diverse range of areas.