Articles | Volume 10, issue 2
Geosci. Model Dev., 10, 811–826, 2017
Geosci. Model Dev., 10, 811–826, 2017

Development and technical paper 21 Feb 2017

Development and technical paper | 21 Feb 2017

Enabling BOINC in infrastructure as a service cloud system

Diego Montes1, Juan A. Añel1,2, Tomás F. Pena3, Peter Uhe4,5, and David C. H. Wallom5 Diego Montes et al.
  • 1EPhysLab, Universidade de Vigo, Ourense, Spain
  • 2Smith School of Enterprise and the Environment, University of Oxford, Oxford, UK
  • 3Centro de Investigación en Tecnoloxías da Información (CITIUS), University of Santiago de Compostela, Santiago de Compostela, Spain
  • 4School of Geography and the Environment, University of Oxford, Oxford, UK
  • 5Oxford e-Research Centre, University of Oxford, Oxford, UK

Abstract. Volunteer or crowd computing is becoming increasingly popular for solving complex research problems from an increasingly diverse range of areas. 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. 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.

We discuss the way in which cloud services can help BOINC-based projects to deliver results in a fast, on demand manner. 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 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.

Short summary
This paper discusses the how the combination of cloud and volunteer computing can be a feasible solution to address large, complex, and expensive computing problems such as climate modelling.