Design and Implementation of a Cloud Infrastructure for Distributed Scientific Calculation

Cloud computing enables independent end users and applications to share data and pooled resources, possibly located in geographically distributed Data Centers, in a fully transparent way. This need is particularly felt by scientific applications to exploit distributed resources in efficient and scalable way for the processing of big amount of data. This paper proposes an open so- lution to deploy a Platform as a service (PaaS) over a set of multi- site data centers by applying open source virtualization tools to facilitate operation among virtual machines while optimizing the usage of distributed resources. An experimental testbed is set up in Openstack environment to obtain evaluations with different types of TCP sample connections to demonstrate the functionality of the proposed solution and to obtain throughput measurements in relation to relevant design parameters.

Using high resolution tracer data to constrain water storage, flux and age estimates in a spatially distributed rainfall-runoff model

Acknowledgements The authors would like to thank Jonathan Dick, Josie Geris, Jason Lessels, and Claire Tunaley for data collection and Audrey Innes for lab sample preparation. We also thank Christian Birkel for discussions about the model structure and comments on an earlier draft of the paper. Climatic data were provided by Iain Malcolm and Marine Scotland Fisheries at the Freshwater Lab, Pitlochry. Additional precipitation data were provided by the UK Meteorological Office and the British Atmospheric Data Centre (BADC).We thank the European Research Council ERC (project GA 335910 VEWA) for funding the VeWa project.

DiCER: A distributed consumer experience research method for use in public spaces

Date of Acceptance: 06/03/2015 Acknowledgement: This research was funded by NCR and the Northern Research Partnership