Name
Abstract | ||
|---|---|---|
Urban flood risk modelling is a highly topical example of intensive computational processing. Such processing is increasingly required by a range of organisations including local government, engineering consultancies and the insurance industry to fulfil statutory requirements and provide professional services. As the demands for this type of work become more common, then ownership of high-end computational resources is warranted but if use is more sporadic and with tight deadlines then the use of Cloud computing could provide a cost-effective alternative. However, uptake of the Cloud by such organisations is often thwarted by the perceived technical barriers to entry. In this paper we present an architecture that helps to simplify the process of performing parameter sweep work on an Infrastructure as a Service Cloud. A parameter sweep version of the urban flood modelling, analysis and visualisation software “CityCat” was developed and deployed to estimate spatial and temporal flood risk at a whole city scale – far larger than had previously been possible. Performing this work on the Cloud allowed us access to more computing power than we would have been able to purchase locally for such a short time-frame (∼21 months of processing in a single calendar month). We go further to illustrate the considerations, both functional and non-functional, which need to be addressed if such an endeavour is to be successfully achieved. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/2192-113X-2-7 | J. Cloud Computing |
Keywords | Field | DocType |
cloud computing | Architecture,Statutory law,Local government,Visualization,Computer science,Operations research,Risk analysis (engineering),Software,Barriers to entry,Flood myth,Distributed computing,Cloud computing | Journal |
Volume | Issue | ISSN |
2 | 1 | 2192-113X |
Citations | PageRank | References |
11 | 0.85 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| V. Glenis | 1 | 17 | 2.19 |
| Andrew Stephen Mcgough | 2 | 105 | 11.18 |
| vedrana kutija | 3 | 11 | 1.18 |
| c g kilsby | 4 | 11 | 0.85 |
| Simon Woodman | 5 | 45 | 4.92 |