1. Scientific data cloud infrastructure and services in Chinese Academy of Sciences Jianhui LI(lijh@cnic.cn),lijh@cnic.cn Yuanke Wei(weiyuanke@cnic.cn)weiyuanke@cnic.cn Yuanchun Zhou(zyc@cnic.cn)zyc@cnic.cn Computer Network Information Center Chinese Academy of Sciences

2. Outline About us –CAS (Chinese Academy of Sciences) –CNIC(Computer Network Information Center), CAS –SDC(Scientific Data Center), CNIC, CAS About Scientific Data Cloud of CAS –Data Challenge –Architecture –Infrastructure Service –Middleware Service –Data Service Conclusion 2

3. CAS is a leading academic institution and comprehensive research and development center in natural science, technological science and high-tech innovation in China. It was founded in Beijing on 1st November 1949 on the basis of the former Academia Sinica (Central Academy of Sciences) and Peiping Academy of Sciences. 3

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5. a public support institution for consistent construction, operation and services of information infrastructure of CAS. a pioneer, promoter and participator for informtion of domestic scientific research and scientific research management 5

6. Operation and Services in CNIC 6 —— Provided by 7 Business Departments Respectively Scientific Research Network EnvironmentScientific Data EnvironmentSupercomputing EnvironmentInformatization of Research ManagementInternet-based Science Popularization and EducationInternet Fundamental Resource Services

7. Scientific Data Center (SDC) is the support facility in charge of the construction, management, operation and maintenance of CAS Informatization Data Application Environment, and has been taking the lead in implementing the CAS Scientific Database Project for more than 20 years. SDC provides storage services, data services and related application technology services for the entire CAS SDC hosts the Secretariat of Committee on Data for Science and Technology (CODATA) and the CAS Secretariat for World Wide Web Consortium (W3C). The vision of SDC is striving to become an important facilitator of exchange and application of scientific data resources, key technology supplier during lifecycle of scientific data, and leader in transforming scientific data into knowledge service. Scientific Data Center 7

8. Outline About us –CAS (Chinese Academy of Sciences) –CNIC(Computer Network Information Center), CAS –SDC(Scientific Data Center), CNIC, CAS About Scientific Data Cloud of CAS –Data Challenge –Architecture –Infrastructure Service –Middleware Service –Data Service Conclusion 8

9. Hotter and hotter in data research Mar.29, 2012, the Obama Administration “ Big Data Research and Development Initiative ”($200 Million) : improving our ability to extract knowledge and insights from large and complex collections of digital data Feb. 11, 2011, 《 Science 》 issued a Special Online Collection: “Dealing with Data” Sep., 2009, 《 Nature 》 issued “Data’s shameful neglect”: Research cannot flourish if data are not preserved and made accessible. All concerned must act accordingly. The Second International Symposium on Dataology & Data Science was held 3 days ago in China Difficult to discover Difficult to access Being lost 9

10. Data Driven Scientific Discovery Data is regarded as the most valuable thing. “The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science." — Bill Gates Scientific discovery based on data intensive computing is now considered as the ''fourth paradigm'' after theoretical, experimental, and computational science. 10

11. Over Moore’s Law in Data IDC: Data doubles less every 18 months Huge volume Rapid increase Various types and formats 11

12. Data Challenge Scientists are being overwhelmed with exploding scientific data. Much scientific research needs data distributed in different locations. There is a growing gap between ability of modern scientific instruments and that of scientists. It has been a great challenge to view, manipulate, store, move, share, and interpret the massive data. 12

13. Scientific Data Deluge in CAS Large scientific facilities produce huge data –+20 being operation –+20 under construction Long-Term field observation stations –+100 stations including Ecology, Environment, Space, etc. Long-Term Research data need to be archived and shared –100+ institutes Large Scientific facilities Field observation stations 13

14. High Speed Network -CSTNET -CSTNET-CNGI -GLORIAD 1.Field observation stations 2.Large scientific facilities 3.others Advanced CI for Data Lifecycle in CAS Application Generation &Collection Trans- mission Computing &Analysis Storage &Curation Data Information Stream Data Centers -storage &preservation -Curation -Sharing and Service Supercomputing Grid -Computing -Analysis -Mining -visualization Data intensive e- Science activities and Applications 14

15. It is mixed evolution of grid computing, distributed computing, parallel computing, utility computing, network storage technologies, virtualization, and etc. It has the characteristics of large-scale, virtualization, high reliability, generality, expandability, on-demand service, extremely cheap, which enables it a popular computing paradigm. It can bridge the scientists and massive data. Chinese Academy of Sciences Scientific Data Cloud (CASSDC) is focused on cloud technology to provide facilitated ways for scientists to make use of powerful information infrastructure, massive scientific data and rich scientific software. Cloud Computing 15

16. Services of CASSDC 16

17. Scientific Data infrastructure Middle ware (Scientific data grid middleware, internet-based storage service middleware…) Scientific databases Massive storage system Data-intensive computing facility High speed network Application enabled environments and typical e-science practice Software and Toolkits (scientific data collection, curation, and publishing, data analyzing and visualization…) 17

18. Data Centers Distribution of CASSDC Scientific Data ~1PB Above 60 institutions Multiple Disciplines Storage Capacity ~ 22PB(50PB) 1 major center 1 archive center 12 middle-size center Computing Capacity ~ 5000(10000) CPU cores Dedicated design for DIC Scientific Data ~1PB Above 60 institutions Multiple Disciplines Storage Capacity ~ 22PB(50PB) 1 major center 1 archive center 12 middle-size center Computing Capacity ~ 5000(10000) CPU cores Dedicated design for DIC 18

19. System Ach. Of Major Center 19

20. Enabling Technology: Infrastructure Global File System of Cloud Storage 20

21. Enabling Technology: Infrastructure On fly provision of a computing cluster 21

22. Scientific Databases (SDB) A Long-term mission started in 1986 which funded by CAS –many institutes involved –long-term, large-scale collaboration –data from research, for research Collecting multi-discipline research data and promoting data sharing –More than 350 research databases and 500 datasets by 61 institutes –Over 200TB data available to open access and download http://www.csdb.cn 22

23. Scientific Databases (cont.) focusing on data integration and improving research database to be resource database and even reference database) Research database Resource database Reference database Application oriented database 23

24. Scientific Databases (cont.) 8 Resource databases –Geo-Science –Biodiversity –Chemistry –Astronomy –Space Science –Micro biology and virus –Material science –Environment  2 Reference databases –China Species –compound  4 application-Oriented databases –High Energy (ITER) –Western Environment Research –Ecology research –Qinghai Lake Research 24

25. Scientific Databases (cont.) 37 research databases –Physics & Chemistry, Geosciences, Biosciences, Atmospheric & Ocean Science, Energy Science, Material Science, Astronomy & Space Science 25

26. CAS Scientific Data Grid SDG is –built upon the Scientific Database, supporting to find and access large scale, distributed and heterogeneous scientific data uniformly and conveniently in a SECURE and proper way Building scientific data application grid according to domain requirements –Integrate distributed data, analysis tools and storage and computing facilities, providing a uniform data service interface –4 pilot grids bioscience grid geoscience grid Chemistry grid Astronomy and space science grid 26

27. Scientific Data Grid-Architecture Organization Architecture of SDG 27

28. SDG-Platform && Middleware Platform –SDGIM: Information Management –SDGOM: Operation Management –SDGSA: Storage Service –SDGMS: Monitor && Statistic Middelware –SDGDD: Data Publish –SDGDT:Data Transfer Toolkit –SDGDC: Data Compress Toolkit –SDGMM:MetaData Management –SDGJS: Job Scheduler 28

29. Tools for data management and service 29

30. An Integrated Case on Geography Supported by CASSDC Data and computing resource are both distributed Model is from CAS scientist Adopted Middleware: Data search Data transport On-fly computing provision Job scheduler It solves massive data computing while some commercial geometric software can’t work Project: High Precision Display of Earth Surface 30

31. Data: Microbiology Institute World Data Center for Microorganisms Wuhan Virus Institute Computing: CNIC Microbiology Institute Adopted Middleware: Data search Data transport Job scheduler User athentication Gene Alignment Project An Integrated Case on Biography Supported by CASSDC 31

32. An Integrated Case on Biography Supported by CASSDC 32

33. Cooperation International Organization Membership 33

34. Cooperation with Europe CSTNET provide network support for the data transmission between Europe and China 34 ITER Global Earth Observation System of Systems CERN LHC: ATLAS & CMS ARGO-Yangbajing

35. Challenges On-demand Linking multi-disciplinary data based on semantic Big Data processing –High scalable, Low cost, high Throughput –On-demand flexible data processing Integrate data, storage, computing, analysis model and etc. as a whole system driven by one specific scientific problem –Making infrastructure invisible for scientists 35

36. Conclusion Science discovery has increasingly become data intensive, and it calls for reliable and easily accessible scientific data infrastructure CAS is always promoting to build scientific data infrastructure and data intensive e-Science practices Seeking potential cooperation in data intensive e-Science and data cloud 36

37. Thank you! 37