1. Pararel Computing for Scientific Environment Cluster, Grid & Cloud approach Mardhani Riasetiawan, MT, Candidate Ph.D mardhani@ugm.ac.id http://mardhani.blog.ugm.ac.id 6283869942863 Department of Computer Science & Electronics Faculty of Mathematic and Natural Science Universitas Gadjah Mada www.dcse.fmipa.ugm.ac.id A Research and Working Group on Grid & Cloud Technology Universitas Gadjah Mada www.cloud.wg.ugm.ac.id

2. AGENDA Konsep Kenapa Pararel, Cloud Computing? Cloud Computing Cluster, Grid, & Cloud Spatial Cloud Computing Best Practise GEOSS Clearing House Dala Project GamaBox Implementasi Ide & isu Arsitektur Teknologi Case study

3. Teknologi Memaksimalkan sumber daya dan meminimalisir resiko

4. Isu Teknologi The digital universe will grow 10-fold in five years, from ~160-170 exabytes in 2006 to >1,600 exabytes in 2011 Information created surpassed available storage in 2007, will be 2X five years Unstructured information accounts for >90% of the digital universe Consumers/individuals account for ~70% of information created, yet enterprises have “responsibility/liability” for ~85% Preservation “intense” information will grow 9-fold in 5 years The Issues 4 Transient information or unfilled demand for storage Information Available Storage Petabytes Worldwide Source: John Gantz, Chief Research Officer, IDC

5. “Enabler”

6. Fakta tentang Data

7. Cluster – Grid – Cloud Un-used & second hard hardware Cluster ComputerGrid ComputerCloud Technology

8. Pararel Computing Yang ditawarkan Integrasi semua data geospatial, pengetahuan/knowledge, dan memprosesnya dengan waktu yang terukur. Menghasilkan dan mengirimkan informasi yang benar secara real-timekepada pengambil keputusan, penguna utama dan korban. Platform dan infrastruktur komputasi Siap dalam beberapa menit Dapat mengakomodasi kebutuhan penguna Mengeluarkan sesuai dengan “biaya” komputasi yang digunakan Menghindari emergency cost yang muncul dari kegagalan sistem yang sudah ada

9. By definition “Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.” (NIST 2010)

11. Spatial Cloud Computing Data Intensity Computing Intensity Concurrent Access Intensity, and Spatiotemporal Intensity Enables the geospatial science discoveries, emergency responses, education, other societal benefits Is optimized by spatiotemporal principles.

12. Spatial Databases: Representative Projects only in old plan Only in new plan In both plans Evacutation Route Planning Parallelize Range Queries Storing graphs in disk blocksShortest Paths

13. Why cloud computing for spatial data? Geospatial Intelligence [ Dr. M. Pagels, DARPA, 2006] Estimated at 140 terabytes per day, 150 peta-bytes annually Annual volume is 150x historical content of the entire internet Analyze daily data as well as historical data

14. Best Practices

15.  Advanced Computing Technologies Cloud Computing (EC2 & Azure) Responds to Spike Massive Concurrent End Users Cloud Computing (EC2 & Azure) Responds to Spike Massive Concurrent End Users Cloud DB (SQLAzure) Manages Millions to Billions of Metadata Records Cloud DB (SQLAzure) Manages Millions to Billions of Metadata Records WebGIS & 5D Vis Tools to Visualizes EO Data WebGIS & 5D Vis Tools to Visualizes EO Data GEOSS Clearinghouse  Objectives  Share Global Earth Observation Data Among 140+ Countries to Address Global Challenges of Natural Hazards and Emergency Responses  Support Global End Users to Discover, Access, and Utilize EO Data  Provide Responses to End Users in Seconds

16. Concurrent Intensity

17. CERN

18. Implementasi

19. Arsitektur

20. A Conceptual Framework for CloudGIS Yang C., Bambacus M., Benedict K., Nebert D., Mochuney D., Hazlett S., Houser P., Raskin R., Xu Y., Fay D., Rezgui A., Huang Q., and Xu C., 2011. Using Metadata, Data/Service Quality and Knowledge to Facilitate Better Data Discovery, Access, and Utilization for Supporting EarthCube, http://semanticommunity.info/@api/deki/files/13812/=024_Yang.pdf. http://semanticommunity.info/@api/deki/files/13812/=024_Yang.pdf

21. Referensi 1. Yang, C., Goodchild M., Huang Q., Nebert D., Raskin R., Xu Y., Bambacus M., Fay D., 2011a, Spatial Cloud Computing: How could geospatial sciences use and help to shape cloud computing, International Journal on Digital Earth.Spatial Cloud Computing: How could geospatial sciences use and help to shape cloud computing, International Journal on Digital Earth 2. Foster, I., Zhao, Y., Raicu, Y., Lu, S., 2008. Cloud Computing and Grid Computing 360-Degree Compared, In: Grid Computing Environments Workshop, GCE 2008. IEEE, Los Alamitos.Cloud Computing and Grid Computing 360-Degree Compared 3. Yang, C., Raskin, R., Goodchild, M.F., and Gahegan, M., 2010, Geospatial Cyberinfrastructure: Past, Present and Future, Computers, Environment, and Urban Systems, 34(4):264-277.Geospatial Cyberinfrastructure: Past, Present and Future 4. M.F. Goodchild, M. Yuan, and T.J. Cova (2007) Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science 21(3): 239–260. (Open Access)Towards a general theory of geographic representation in GIS 5. Rey, S. J., and M. V. Janikas. 2006. STARS: Space-Time Analysis of Regional Systems. Geographical Analysis, 38 (1): 67– 86.STARS: Space-Time Analysis of Regional Systems 6. Armbrust, M, Fox, A., Griffith R., Joseph A., Katz, R. and etc, 2009. Above the Cloud: A Berkeley View of Cloud Computing, Technical Report No. UCB/EECS-2009-28. (Open Access)Above the Cloud: A Berkeley View of Cloud Computing

22. 7. Wang S. and Armstrong M., 2009. A theoretical approach to the use of cyberinfrastructure in geographical analysis, International Journal of Geographical Information Science 23(2), 169 – 193. (Open Access)A theoretical approach to the use of cyberinfrastructure in geographical analysis 8. Yang C., Wu H., Li Z., Huang Q., Li J., 2011, Spatial Computing: Utilizing Spatial Principles to Optimize Distributed Computing for Enabling Physical Science Discoveries, Proceedings of National Academy of Sciences, doi: 10.1073/pnas.0909315108. (Open Access) http://www.pnas.org/content/early/2011/03/21/0909315108.full.pdfSpatial Computing: Utilizing Spatial Principles to Optimize Distributed Computing for Enabling Physical Science Discoveries http://www.pnas.org/content/early/2011/03/21/0909315108.full.pdf 9. Wang, S., and Liu, Y. 2009. TeraGrid GIScience Gateway: Bridging Cyberinfrastructure and GIScience. International Journal of Geographical Information Science, 23 (5): 631-656.TeraGrid GIScience Gateway: Bridging Cyberinfrastructure and GIScience 10. Evangelinos C., Hill C., 2008. Cloud Computing for parallel Scientific HPC Applications: Feasibility of running Coupled Atmosphere-Ocean Climate Models on Amazon’s EC2, CCA-08 October 22–23, 2008.Cloud Computing for parallel Scientific HPC Applications: Feasibility of running Coupled Atmosphere-Ocean Climate Models on Amazon’s EC2 11. Image taken from : http://www.bluecloudspatial.com/

23. Terima kasih