Cloud Computing for Climate Change Impact Assessment

1. Use of Cloud computing in impact assessment of climate change Kwang Soo Kim and Doug MacKenzie

2. Outline • Introduction • Cloud computing • Case study: Calculation of rainfall frequency in the 21st century • Results • Conclusions

3. Climate Change

4. Impact of climate change Simulation models has been used to assess the impact of Climate change • Geographical distribution of species across a wide range of ecosystems (Walther et al. 2002) • The timing of blooming for temperate zone species (Root et al. 2003) • Crop production in positive or negative ways in different regions in the 21st century (Rosenzweig et al. 2001).

5. Computational scale of impact assessment study • The spatial resolution of GCMs has increased • The typical resolution used in the IPCC TAR was about 250 km (Houghton et al. 2001). • In the AR4, many of those models had higher spatial resolution (Miller et al. 2006). • NCAR-CCSM3 had spatial resolution of 150 km. • An ensemble prediction system has been used • Probabilistic forecasts of climatic events are generated • Murphy et al. (2004) used a 53-member ensemble of models to determine the range of climate changes. • Climate change scenarios • The Special Report on Emission Scenarios (SRES) • B1, A1B and A2

6. Cloud computing • A paradigm of computing in which virtualized resources are provided as a service over the Internet (Gruman & Knorr, 2008) • Computing resources “As a Service” • Infrastructure as a service (IaaS) • Platform as a service (PaaS) • Software as a service (SaaS) • Data storage as a service (dSaaS) • Utility computing • Distributed computing

7. Amazon web service – Elastic Compute Cloud (EC2) • Customers can rent computers on which to run their own computer applications • Scalable deployment of applications by creating virtual machines • A customer can create, launch, and terminate server instances as needed • Customers are charged by the hour for active servers • Hourly charge per virtual machine ($0.10 to $1.2 per hour) • Data transfer charge ($0.10 to $0.17 per gigabyte) • Allocated and unused Elastic IP address • Storage using Amazon Elastic Block Store (EBS) • Additional transfer charges using Elastic Load Balancing • Using Amazon's CloudWatch service to monitor your virtual machine • Using Amazon's Elastic Load Balancing which distributes load among selected virtual machine

8. Science Clouds • The Science Clouds project was initiated by the University of Chicago (UC) and the University of Florida (UFL) • (http://workspace.globus.org/clouds/) • EC2-style cloud computing • members of the scientific community to lease resources for short amounts of time • The Science Clouds do not require users to directly pay for usage • Verify the person asking for an allocation is indeed a member of the scientific community • Ask for a short writeup of the scientific project. • Based on the project the individual is allocated a small (testing), middle (development), or large (science) hour credit on the Science Clouds.

9. Objective • Calculate monthly rainfall frequency in the 21st century • Daily precipitation (P) was calculated • P = RF * 86400 • RF = daily rainfall flux • It was assumed that rainfall occurred on a day when P > 0.254 mm

10. Climate projection data • The daily sets of GCM outputs were obtained from the WCRP CMIP3 multi-model database (https://esgcet.llnl.gov:8443).

11. System architecture

12. System configuration • Amazon web service EC2 • Small instances for Server and Client images • Server • Fedora 8 • MySQL database server • Network file system (NFS) service • Clients • Ubuntu 8.10 • A script to download a daily climate change dataset from the internet. • The data process program • using NetCDF file format • search and extract subsets of the original dataset.

13. Results • The running time using 10 client instances was about 32 hr. • Downloading the climate data • Between 4.8 hr and 9.1 hr • Total time was 66 hr • Database transaction • Between 15.2 hr and 24.8 hr • Total time was 209 hr

14. Running time

15. File size and transfer rate

16. Database transaction

17. Computation cost (USD) • CPU: • Downloading/Processing data: (275 hr + 32 hr) x $ 0.1 = $ 30.7 • Processing/Downloading results: 42 hr x $ 0.1 = $ 4.2 • Transfer: • 70 GB x $ 0.1 = $ 7 • Storage: • Climate data storage: 30 GB x $ 0.1 = $ 3 • Database storage: 10 GB x $ 0.1 = $ 1 • Total cost • $ 46

18. Conclusions • Cloud computing could provide inexpensive and temporary computing resources to analyse large-scale scientific data for the climate change impact assessment. • In a 10 processor-core configuration, our approach would be up to 10 times faster than the calculation on a single processor core machine. • The costs for processor core use, data transfer and temporary storage were about $35, $7 and $4, respectively. • Cloud computing have benefits • Running time • Local storage resources • Network resources.

19. Acknowledgement • New Zealand’s Foundation for Research,Science and Technology through contract CO2X050, Better Border Biosecurity (B3) (www.b3nz.org).

20. The New Zealand Institute for Plant & Food Research Limited Questions ? Email: kwang.kim@plantandfood.co.nz