1. TICER Summer School, August 24th 20061 Ticer Summer School Thursday 24 th August 2006 Dave Berry & Malcolm Atkinson National e-Science Centre, Edinburgh www.nesc.ac.uk

2. TICER Summer School, August 24th 20062 Digital Libraries, Grids & E-Science What is E-Science? What is Grid Computing? Data Grids Requirements Examples Technologies Data Virtualisation The Open Grid Services Architecture Challenges

3. TICER Summer School, August 24th 20063

4. 4 What is e-Science? Goal: to enable better research in all disciplines Method: Develop collaboration supported by advanced distributed computation –to generate, curate and analyse rich data resources From experiments, observations, simulations & publications Quality management, preservation and reliable evidence –to develop and explore models and simulations Computation and data at all scales Trustworthy, economic, timely and relevant results –to enable dynamic distributed collaboration Facilitating collaboration with information and resource sharing Security, trust, reliability, accountability, manageability and agility

5. climateprediction.net and GENIE Largest climate model ensemble >45,000 users, >1,000,000 model years 10K 2K Response of Atlantic circulation to freshwater forcing

6. 6 Courtesy of David Gavaghan & IB Team Integrative Biology Tackling two Grand Challenge research questions: What causes heart disease? How does a cancer form and grow? Together these diseases cause 61% of all UK deaths Building a powerful, fault-tolerant Grid infrastructure for biomedical science Enabling biomedical researchers to use distributed resources such as high-performance computers, databases and visualisation tools to develop coupled multi-scale models of how these killer diseases develop.

7. Biomedical Research Informatics Delivered by Grid Enabled Services Synteny Grid Service blast + Portal http://www.brc.dcs.gla.ac.uk/projects/bridges/

8. TICER Summer School, August 24th 20068 eDiaMoND: Screening for Breast Cancer 1 Trust  Many Trusts Collaborative Working Audit capability Epidemiology Other Modalities -MRI -PET -Ultrasound Better access to Case information And digital tools Supplement Mentoring With access to digital Training cases and sharing Of information across clinics Letters Radiology reporting systems eDiaMoND Grid 2ndary Capture Or FFD Case Information X-Rays and Case Information Digital Reading SMF Case and Reading Information CADTemporal Comparison Screening Electronic Patient Records Assessment/ Symptomatic Biopsy Case and Reading Information Symptomatic/Assessment Information Training Manage Training Cases Perform Training SMF CAD 3D Images Patients Provided by eDiamond project: Prof. Sir Mike Brady et al.

9. TICER Summer School, August 24th 20069 E-Science Data Resources Curated databases –Public, institutional, group, personal Online journals and preprints Text mining and indexing services Raw storage (disk & tape) Replicated files Persistent archives Registries …

10. TICER Summ er School, August 24th 2006© 10 EBank Slide from Jeremy Frey

11. TICER Summ er School, August 24th 2006© 11 Biomedical data – making connections 12181 acatttctac caacagtgga tgaggttgtt ggtctatgtt ctcaccaaat ttggtgttgt 12241 cagtctttta aattttaacc tttagagaag agtcatacag tcaatagcct tttttagctt 12301 gaccatccta atagatacac agtggtgtct cactgtgatt ttaatttgca ttttcctgct 12361 gactaattat gttgagcttg ttaccattta gacaacttca ttagagaagt gtctaatatt 12421 taggtgactt gcctgttttt ttttaattgg Slide provided by Carole Goble: University of Manchester

12. TICER Summer School, August 24th 200612 Using Workflows to Link Services Describe the steps in a Scripting Language Steps performed by Workflow Enactment Engine Many languages in use –Trade off: familiarity & availability –Trade off: detailed control versus abstraction Incrementally develop correct process –Sharable & Editable –Basis for scientific communication & validation –Valuable IPR asset Repetition is now easy –Parameterised explicitly & implicitly

13. TICER Summer School, August 24th 200613 Workflow Systems LanguageWF Enact.Comments Shell scripts Shell + OSCommon but not often thought of as WF. Depend on context, e.g. NFS across all sites PerlPerl runtime Popular in bioinformatics. Similar context dependence – distribution has to be coded JavaJVMPopular target because JVM ubiquity – similar dependence – distribution has to be coded BPELBPEL Enactment OASIS standard for industry – coordinating use of multiple Web Services – low level detail - tools TavernaScuflEBI, OMII-UK & MyGrid http://taverna.sourceforge.net/index.php http://taverna.sourceforge.net/index.php VDT / Pegasus Chimera & DAGman High-level abstract formulation of workflows, automated mapping towards executable forms, cached result re-use Kepler BIRN, GEON & SEEK http://kepler-project.org/

14. TICER Summ er School, August 24th 2006© 14 Workflow example Taverna in MyGrid http://www.mygrid.org.uk/http://www.mygrid.org.uk/ “allows the e-Scientist to describe and enact their experimental processes in a structured, repeatable and verifiable way” GUI Workflow language Enactment engine

15. TICER Summ er School, August 24th 2006© 15 Pub/Sub for Laboratory data using a broker and ultimately delivered over GPRS Notification Comb-e-chem: Jeremy Frey

16. TICER Summer School, August 24th 200616 Relevance to Digital Libraries Similar concerns –Data curation & management –Metadata, discovery –Secure access (AAA +) –Provenance & data quality –Local autonomy –Availability, resilience Common technology –Grid as an implementation technology

17. TICER Summer School, August 24th 200617

18. TICER Summer School, August 24th 200618 What is a Grid? License Printer A grid is a system consisting of −Distributed but connected resources and −Software and/or hardware that provides and manages logically seamless access to those resources to meet desired objectives A grid is a system consisting of −Distributed but connected resources and −Software and/or hardware that provides and manages logically seamless access to those resources to meet desired objectives R2AD Database Web server Data CenterCluster HandheldSupercomputer Workstation Server Source: Hiro Kishimoto GGF17 Keynote May 2006

19. TICER Summer School, August 24th 200619 Virtualizing Resources Resources Web services Access Storage Sensors Applications Information Computers Resource-specific Interfaces Common Interfaces Type-specific interfaces Hiro Kishimoto: Keynote GGF17

20. TICER Summer School, August 24th 200620 Ideas and Forms Key ideas –Virtualised resources –Secure access –Local autonomy Many forms –Cycle stealing –Linked supercomputers –Distributed file systems –Federated databases –Commercial data centres –Utility computing

21. TICER Summer School, August 24th 200621 Grid Middleware Virtualized resources Grid middleware services Brokering Service Registry Service Data Service CPU Resource Printer Service Job-Submit Service Compute Service Notify Advertise Application Service Hiro Kishimoto: Keynote GGF17

22. TICER Summer School, August 24th 200622 Key Drivers for Grids Collaboration –Expertise is distributed –Resources (data, software licences) are location-specific –Necessary to achieve critical mass of effort –Necessary to raise sufficient resources Computational Power –Rapid growth in number of processors –Powered by Moore’s law + device roadmap –Challenge to transform models to exploit this Deluge of Data –Growth in scale: Number and Size of resources –Growth in complexity –Policy drives greater data availability

23. TICER Summer School, August 24th 200623 Minimum Grid Functionalities Supports distributed computation –Data and computation –Over a variety of hardware components (servers, data stores, …) Software components (services: resource managers, computation and data services) –With regularity that can be exploited By applications By other middleware & tools By providers and operations –It will normally have security mechanisms To develop and sustain trust regimes

24. TICER Summer School, August 24th 200624 Source: Hiro Kishimoto GGF17 Keynote May 2006 Grid & Related Paradigms Utility Computing Computing “services” No knowledge of provider Enabled by grid technology Utility Computing Computing “services” No knowledge of provider Enabled by grid technology Distributed Computing Loosely coupled Heterogeneous Single Administration Distributed Computing Loosely coupled Heterogeneous Single Administration Cluster Tightly coupled Homogeneous Cooperative working Cluster Tightly coupled Homogeneous Cooperative working Grid Computing Large scale Cross-organizational Geographical distribution Distributed Management Grid Computing Large scale Cross-organizational Geographical distribution Distributed Management

25. TICER Summer School, August 24th 200625

26. TICER Summer School, August 24th 200626 Why use / build Grids? Research Arguments –Enables new ways of working –New distributed & collaborative research –Unprecedented scale and resources Economic Arguments –Reduced system management costs –Shared resources  better utilisation –Pooled resources  increased capacity –Load sharing & utility computing –Cheaper disaster recovery

27. TICER Summer School, August 24th 200627 Why use / build Grids? Operational Arguments –Enable autonomous organisations to Write complementary software components Set up run & use complementary services Share operational responsibility General & consistent environment for Abstraction, Automation, Optimisation & Tools Political & Management Arguments –Stimulate innovation –Promote intra-organisation collaboration –Promote inter-enterprise collaboration

28. TICER Summer School, August 24th 200628 Grids In Use: E-Science Examples Data sharing and integration −Life sciences, sharing standard data-sets, combining collaborative data-sets −Medical informatics, integrating hospital information systems for better care and better science −Sciences, high-energy physics Data sharing and integration −Life sciences, sharing standard data-sets, combining collaborative data-sets −Medical informatics, integrating hospital information systems for better care and better science −Sciences, high-energy physics Capability computing −Life sciences, molecular modeling, tomography −Engineering, materials science −Sciences, astronomy, physics Capability computing −Life sciences, molecular modeling, tomography −Engineering, materials science −Sciences, astronomy, physics High-throughput, capacity computing for −Life sciences: BLAST, CHARMM, drug screening −Engineering: aircraft design, materials, biomedical −Sciences: high-energy physics, economic modeling High-throughput, capacity computing for −Life sciences: BLAST, CHARMM, drug screening −Engineering: aircraft design, materials, biomedical −Sciences: high-energy physics, economic modeling Simulation-based science and engineering −Earthquake simulation Simulation-based science and engineering −Earthquake simulation Source: Hiro Kishimoto GGF17 Keynote May 2006

29. TICER Summer School, August 24th 200629

30. PDB 33,367 Protein structures EMBL DB 111,416,302,701 nucleotides Database Growth Slide provided by Richard Baldock: MRC HGU Edinburgh

31. TICER Summer School, August 24th 200631 Requirements: User’s viewpoint Find Data –Registries & Human communication Understand data –Metadata description, Standard / familiar formats & representations, Standard value systems & ontologies Data Access –Find how to interact with data resource –Obtain permission (authority) –Make connection –Make selection Move Data –In bulk or streamed (in increments)

32. TICER Summer School, August 24th 200632 Requirements: User’s viewpoint 2 Transform Data –To format, organisation & representation required for computation or integration Combine data –Standard database operations + operations relevant to the application model Present results –To humans: data movement + transform for viewing –To application code: data movement + transform to the required format –To standard analysis tools, e.g. R –To standard visualisation tools, e.g. Spitfire

33. TICER Summer School, August 24th 200633 Requirements: Owner’s viewpoint Create Data –Automated generation, Accession Policies, Metadata generation –Storage Resources Preserve Data –Archiving –Replication –Metadata –Protection Provide Services with available resources –Definition & implementation: costs & stability –Resources: storage, compute & bandwidth

34. TICER Summer School, August 24th 200634 Requirements: Owner’s viewpoint 2 Protect Services –Authentication, Authorisation, Accounting, Audit –Reputation Protect data –Comply with owner requirements – encryption for privacy, … Monitor and Control use –Detect and handle failures, attacks, misbehaving users –Plan for future loads and services Establish case for Continuation –Usage statistics –Discoveries enabled

35. TICER Summer School, August 24th 200635

36. TICER Summer School, August 24th 200636 Large Hadron Collider The most powerful instrument ever built to investigate elementary particle physics Data Challenge: –10 Petabytes/year of data –20 million CDs each year! Simulation, reconstruction, analysis: –LHC data handling requires computing power equivalent to ~100,000 of today's fastest PC processors

37. TICER Summer School, August 24th 200637 Composing Observations in Astronomy Data and images courtesy Alex Szalay, John Hopkins No. & sizes of data sets as of mid-2002, grouped by wavelength 12 waveband coverage of large areas of the sky Total about 200 TB data Doubling every 12 months Largest catalogues near 1B objects

38. GODIVA Data Portal Grid for Ocean Diagnostics, Interactive Visualisation and Analysis Daily Met Office Marine Forecasts and gridded research datasets National Centre for Ocean Forecasting ~3Tb climate model datastore via Web Services Interactive Visualisations inc. Movies ~ 30 accesses a day worldwide Other GODIVA software produces 3D/4D Visualisations reading data remotely via Web Services Online Movies www.nerc-essc.ac.uk/godiva

39. GODIVA Visualisations Unstructured Meshes Grid Rotation/Interpolation GeoSpatial Databases v. Files (Postgres, IBM, Oracle) Perspective 3D Visualisation Google maps viewer

40. NERC Data Grid The DataGrid focuses on federation of NERC Data Centres Grid for data discovery, delivery and use across sites Data can be stored in many different ways (flat files, databases…) Strong focus on Metadata and Ontologies Clear separation between discovery and use of data. Prototype focussing on Atmospheric and Oceanographic data www.ndg.nerc.ac.uk

41. Global In-flight Engine Diagnostics in-flight data airline maintenance centre ground station global network eg SITA internet, e-mail, pager DS&S Engine Health Center data centre Distributed Aircraft Maintenance Environment: Leeds, Oxford, Sheffield &York, Jim Austin 100,000 aircraft 0.5 GB/flight 4 flights/day 200 TB/day Now BROADEN Significant in getting Boeing 787 engine contract

42. TICER Summer School, August 24th 200642

43. TICER Summer School, August 24th 200643 Storage Resource Manager (SRM) http://sdm.lbl.gov/srm-wg/ de facto & written standard in physics, … Collaborative effort –CERN, FNAL, JLAB, LBNL and RALCERN, FNAL, JLAB, LBNL and RAL Essential bulk file storage –(pre) allocation of storage abstraction over storage systems –File delivery / registration / access –Data movement interfaces E.g. gridFTP Rich function set –Space management, permissions, directory, data transfer & discovery

44. TICER Summer School, August 24th 200644 Storage Resource Broker (SRB) http://www.sdsc.edu/srb/index.php/Main_Page SDSC developed Widely used –Archival document storage –Scientific data: bio-sciences, medicine, geo-sciences, … Manages –Storage resource allocation abstraction over storage systems –File storage –Collections of files –Metadata describing files, collections, etc. –Data transfer services

45. TICER Summer School, August 24th 200645 Condor Data Management Stork –Manages File Transfers –May manage reservations Nest –Manages Data Storage –C.f. GridFTP with reservations Over multiple protocols

46. TICER Summer School, August 24th 200646 Globus Tools and Services for Data Management l GridFTP u A secure, robust, efficient data transfer protocol l The Reliable File Transfer Service (RFT) u Web services-based, stores state about transfers l The Data Access and Integration Service (OGSA-DAI) u Service to access to data resources, particularly relational and XML databases l The Replica Location Service (RLS) u Distributed registry that records locations of data copies l The Data Replication Service u Web services-based, combines data replication and registration functionality Slides from Ann Chervenak

47. TICER Summer School, August 24th 200647 RLS in Production Use: LIGO l Laser Interferometer Gravitational Wave Observatory Currently use RLS servers at 10 sites u Contain mappings from 6 million logical files to over 40 million physical replicas l Used in customized data management system: the LIGO Lightweight Data Replicator System (LDR) u Includes RLS, GridFTP, custom metadata catalog, tools for storage management and data validation Slides from Ann Chervenak