TICER Summer School, August 24th 20061 Ticer Summer School Thursday 24 th August 2006 Dave Berry & Malcolm Atkinson National e-Science Centre, Edinburgh.
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TICER Summer School, August 24th 2006 1
Ticer Summer School
Thursday 24th August 2006
Dave Berry & Malcolm AtkinsonNational e-Science Centre, Edinburghwww.nesc.ac.uk
TICER Summer School, August 24th 2006 2
Digital Libraries, Grids & E-ScienceDigital 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
TICER Summer School, August 24th 2006 3
TICER Summer School, August 24th 2006 4
What is e-Science?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
climateprediction.net and GENIE
• Largest climate model ensemble
• >45,000 users, >1,000,000 model years
10K2K
Response of Atlantic circulation to freshwater forcing
6Courtesy 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.
BBiomedical iomedical RResearch esearch IInformatics nformatics DDelivered by elivered by GGrid rid EEnabled nabled SServiceservices
Glasgow Edinburgh
Leicester Oxford
London
Netherlands
Publically Curated Data
Private data
Private data
Private data
Private data
Private data
Private data
CFG Virtual Organisation Ensembl
MGI
HUGO
OMIM
SWISS-PROT
… DATA HUB
RGD
SyntenyGrid
Service
blast
+
Portal
http://www.brc.dcs.gla.ac.uk/projects/bridges/
TICER Summer School, August 24th 2006 8
eDiaMoND: Screening for Breast CancereDiaMoND: Screening for Breast Cancer
1 Trust Many TrustsCollaborative WorkingAudit capabilityEpidemiology
Other Modalities-MRI-PET-Ultrasound
Better access toCase informationAnd digital tools
Supplement MentoringWith access to digitalTraining cases and sharingOf information acrossclinics
LettersRadiology reportingsystems
eDiaMoNDGrid
2ndary CaptureOr FFD
Case Information
X-Rays andCase Information
DigitalReading
SMF
Case andReading Information
CAD Temporal Comparison
Screening
ElectronicPatient Records
Assessment/ SymptomaticBiopsy
Case andReading Information
Symptomatic/AssessmentInformation
Training
Manage Training Cases
Perform Training
SMF CAD 3D Images
Patients
Provided by eDiamond project: Prof. Sir Mike Brady et al.
TICER Summer School, August 24th 2006 9
E-Science Data ResourcesE-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
• …
TICER Summer School, August 24th 2006
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EBank
Slide from Jeremy Frey
TICER Summer School, August 24th 2006
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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
TICER Summer School, August 24th 2006 12
Using Workflows to Link ServicesUsing 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
TICER Summer School, August 24th 2006 13
Workflow SystemsWorkflow Systems
Language WF Enact. Comments
Shell scripts
Shell + OS Common but not often thought of as WF. Depend on context, e.g. NFS across all sites
Perl Perl runtime
Popular in bioinformatics. Similar context dependence – distribution has to be coded
Java JVM Popular target because JVM ubiquity – similar dependence – distribution has to be coded
BPEL BPEL Enactment
OASIS standard for industry – coordinating use of multiple Web Services – low level detail - tools
Taverna Scufl EBI, OMII-UK & MyGrid 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 Kepler BIRN, GEON & SEEKhttp://kepler-project.org/
TICER Summer School, August 24th 2006
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Workflow example
Taverna in MyGrid 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
TICER Summer School, August 24th 2006
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Pub/Sub for Laboratory data using a broker and ultimately delivered over GPRS
Notification
Comb-e-chem: Jeremy Frey
TICER Summer School, August 24th 2006 16
Relevance to Digital LibrariesRelevance 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
TICER Summer School, August 24th 2006 17
TICER Summer School, August 24th 2006 18
What is a Grid?
LicenseLicense
PrinterPrinter
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
DatabaseDatabase
Webserver
Webserver
Data CenterCluster
Handheld Supercomputer
Workstation
Server
Source: Hiro Kishimoto GGF17 Keynote May 2006
TICER Summer School, August 24th 2006 19
Virtualizing Resources
Resources
Webservices
AccessAccess
StorageStorage SensorsSensors ApplicationsApplications InformationInformationComputersComputers
Resource-specific InterfacesResource-specific Interfaces
Common Interfaces
Type-specific interfaces
Hiro Kishimoto: Keynote GGF17
TICER Summer School, August 24th 2006 20
Ideas and FormsIdeas 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
TICER Summer School, August 24th 2006 21
Grid Middleware
Virtualized resources
Grid middleware
services
Brokering Service
Brokering Service
Registry Service
Registry Service
DataService
DataService
CPU ResourceCPU ResourcePrinter Service
Printer Service
Job-Submit Service
Job-Submit Service
ComputeService
ComputeService
No
tify
Ad
vertise
ApplicationService
ApplicationService
Hiro Kishimoto: Keynote GGF17
TICER Summer School, August 24th 2006 22
Key Drivers for GridsKey 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
TICER Summer School, August 24th 2006 23
Minimum Grid FunctionalitiesMinimum 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
TICER Summer School, August 24th 2006 24Source: 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
TICER Summer School, August 24th 2006 25
TICER Summer School, August 24th 2006 26
Why use / build Grids?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
TICER Summer School, August 24th 2006 27
Why use / build Grids?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
TICER Summer School, August 24th 2006 28
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
TICER Summer School, August 24th 2006 29
PDB 33,367 Protein structuresEMBL DB 111,416,302,701 nucleotides
Database GrowthDatabase Growth
Slide provided by Richard Baldock: MRC HGU Edinburgh
TICER Summer School, August 24th 2006 31
Requirements: User’s viewpointRequirements: 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)
TICER Summer School, August 24th 2006 32
Requirements: User’s viewpoint 2Requirements: 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
TICER Summer School, August 24th 2006 33
Requirements: Owner’s viewpointRequirements: 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
TICER Summer School, August 24th 2006 34
Requirements: Owner’s viewpoint 2Requirements: 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
TICER Summer School, August 24th 2006 35
TICER Summer School, August 24th 2006 36
Large Hadron ColliderLarge 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
TICER Summer School, August 24th 2006 37
Composing Observations in AstronomyComposing 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
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
GODIVA Visualisations• Unstructured Meshes
• Grid Rotation/Interpolation
• GeoSpatial Databases v. Files (Postgres, IBM, Oracle)• Perspective 3D Visualisation
• Google maps viewer
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
Global In-flight Engine DiagnosticsGlobal In-flight Engine Diagnostics
in-flight data
airline
maintenance centre
ground station
global networkeg 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 ingetting Boeing 787 engine contract
TICER Summer School, August 24th 2006 42
TICER Summer School, August 24th 2006 43
Storage Resource Manager (SRM)Storage Resource Manager (SRM)
• http://sdm.lbl.gov/srm-wg/• de facto & written standard in physics, …• Collaborative effort
– CERN, 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
TICER Summer School, August 24th 2006 44
Storage Resource Broker (SRB)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
TICER Summer School, August 24th 2006 45
Condor Data ManagementCondor Data Management
• Stork– Manages File Transfers– May manage reservations
• Nest– Manages Data Storage– C.f. GridFTP with reservations
• Over multiple protocols
TICER Summer School, August 24th 2006 46
Globus Tools and Services for Data Management
GridFTP A secure, robust, efficient data transfer protocol
The Reliable File Transfer Service (RFT) Web services-based, stores state about transfers
The Data Access and Integration Service (OGSA-DAI) Service to access to data resources, particularly relational and
XML databases
The Replica Location Service (RLS) Distributed registry that records locations of data copies
The Data Replication Service Web services-based, combines data replication and
registration functionality
Slides from Ann Chervenak
TICER Summer School, August 24th 2006 47
RLS in Production Use: LIGO
Laser Interferometer Gravitational Wave Observatory Currently use RLS servers at 10 sites
Contain mappings from 6 million logical files to over 40 million physical replicas
Used in customized data management system: the LIGO Lightweight Data Replicator System (LDR)
Includes RLS, GridFTP, custom metadata catalog, tools for storage management and data validation
Slides from Ann Chervenak
TICER Summer School, August 24th 2006 48
RLS in Production Use: ESG
Earth System Grid: Climate modeling data (CCSM, PCM, IPCC)
RLS at 4 sites Data management coordinated
by ESG portal Datasets stored at NCAR
64.41 TB in 397253 total files 1230 portal users
IPCC Data at LLNL 26.50 TB in 59,300 files 400 registered users Data downloaded: 56.80 TB in
263,800 files Avg. 300GB downloaded/day 200+ research papers being
written
Slides from Ann Chervenak
TICER Summer School, August 24th 20062nd EGEE Review, CERN - gLite Middleware Status
49
Enabling Grids for E-sciencE
INFSO-RI-508833
gLite Data Management
• FTS– File Transfer Service
• LFC– Logical file catalogue
• Replication Service– Accessed through LFC
• AMGA– Metadata services
TICER Summer School, August 24th 20062nd EGEE Review, CERN - gLite Middleware Status
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Enabling Grids for E-sciencE
INFSO-RI-508833
Data Management Services
• FiReMan catalog– Resolves logical filenames (LFN) to physical location of files and storage elements– Oracle and MySQL versions available– Secure services– Attribute support– Symbolic link support– Deployed on the Pre-Production Service and DILIGENT testbed
• gLite I/O– Posix-like access to Grid files– Castor, dCache and DPM support – Has been used for the BioMedical Demo– Deployed on the Pre-Production Service and the DILIGENT testbed
• AMGA MetaData Catalog– Used by the LHCb experiment– Has been used for the BioMedical Demo
Medical Data Management 3
Enabling Grids for E-sciencE
ClientClient
Medical Data Management
Application
MDM Client LibraryMDM Client Library
Grid CatalogsGrid Catalogs
MetadataMetadataCatalog (AMGA)Catalog (AMGA)
MedicalImager
EncryptionEncryptionKeystoreKeystore (Hydra)(Hydra)
File CatalogFile Catalog(Fireman)(Fireman)
SRM DICOMSRM DICOM
MDM TriggerMDM Trigger
GridFTPGridFTP
gLitegLite I/OI/O
Trigger:
• Retrieve DICOM files from imager.
• Register file in Fireman
• gLite EDS client: Generate encryption keys and store them in Hydra
• Register Metadata in AMGA
Client Library:
• Lookup file through Metadata (AMGA)
• Use gLite EDS client:
• Retrieve file through gLite I/O
• Retrieve encryption Key from Hydra
• Decrypt data
• Serve it up to the application
TICER Summer School, August 24th 20062nd EGEE Review, CERN - gLite Middleware Status
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Enabling Grids for E-sciencE
INFSO-RI-508833
File Transfer Service
• Reliable file transfer• Full scalable implementation
– Java Web Service front-end, C++ Agents, Oracle or MySQL database support– Support for Channel, Site and VO management– Interfaces for management and statistics monitoring
• Gsiftp, SRM and SRM-copy support• Support for MySQL and Oracle• Multi-VO support• GridFTP and SRM copy support
TICER Summer School, August 24th 2006 52
Commercial SolutionsCommercial Solutions
• Vendors include:– Avaki– Data Synapse
• Benefits & costs– Well packaged and documented– Support– Can be expensive
• But look for academic rates
TICER Summer School, August 24th 2006 53
TICER Summer School, August 24th 2006 54
Data Integration StrategiesData Integration Strategies
• Use a Service provided by a Data Owner
• Use a scripted workflow• Use data virtualisation services
– Arrange that multiple data services have common properties
– Arrange federations of these– Arrange access presenting the common
properties– Expose the important differences– Support integration accommodating those
differences
TICER Summer School, August 24th 2006 55
Data Virtualisation ServicesData Virtualisation Services
• Form a federation– Set of data resources – incremental addition– Registration & description of collected resources– Warehouse data or access dynamically to obtain updated data– Virtual data warehouses – automating division between collection and
dynamic access • Describe relevant relationships between data sources
– Incremental description + refinement / correction• Run jobs, queries & workflows against combined set of data
resources– Automated distribution & transformation
• Example systems– IBM’s Information Integrator– GEON, BIRN & SEEK– OGSA-DAI is an extensible framework for building such systems
TICER Summer School, August 24th 2006 56
Virtualisation variationsVirtualisation variations
• Extent to which homogeneity obtained– Regular representation choices – e.g. units– Consistent ontologies– Consistent data model– Consistent schema – integrated super-schema– DB operations supported across federation– Ease of adding federation elements– Ease of accommodating change as federation
members change their schema and policies– Drill through to primary forms supported
TICER Summer School, August 24th 2006 57
OGSA-DAIOGSA-DAI
• http://www.ogsadai.org.uk • A framework for data virtualisation• Wide use in e-Science
– BRIDGES, GEON, CaBiG, GeneGrid, MyGrid, BioSimGrid, e-Diamond, IU RGRBench, …
• Collaborative effort– NeSC, EPCC, IBM, Oracle, Manchester, Newcastle
• Querying of data resources– Relational databases– XML databases– Structured flat files
• Extensible activity documents– Customisation for particular applications
TICER Summer School, August 24th 2006 58
TICER Summer School, August 24th 2006 59
The Open Grid Services Architecture
• An open, service-oriented architecture (SOA)− Resources as first-class entities− Dynamic service/resource creation and destruction
• Built on a Web services infrastructure
• Resource virtualization at the core
• Build grids from small number of standards-based components− Replaceable, coarse-grained− e.g. brokers
• Customizable− Support for dynamic, domain-specific content…− …within the same standardized framework
Hiro Kishimoto: Keynote GGF17
TICER Summer School, August 24th 2006 60
OGSA Capabilities
Security• Cross-organizational users• Trust nobody• Authorized access only
Security• Cross-organizational users• Trust nobody• Authorized access only
Information Services• Registry• Notification• Logging/auditing
Information Services• Registry• Notification• Logging/auditing
Execution Management• Job description & submission• Scheduling• Resource provisioning
Execution Management• Job description & submission• Scheduling• Resource provisioning
Data Services• Common access facilities• Efficient & reliable transport• Replication services
Data Services• Common access facilities• Efficient & reliable transport• Replication services
Self-Management• Self-configuration• Self-optimization• Self-healing
Self-Management• Self-configuration• Self-optimization• Self-healing
Resource Management• Discovery• Monitoring• Control
Resource Management• Discovery• Monitoring• Control
OGSAOGSA
OGSA “profiles”OGSA “profiles”
Web services foundation Web services foundation
Hiro Kishimoto: Keynote GGF17
TICER Summer School, August 24th 2006 61
Basic Data Interfaces
• Storage Management− e.g. Storage Resource
Management (SRM)
• Storage Management− e.g. Storage Resource
Management (SRM)
• Data Access− ByteIO− Data Access & Integration
(DAI)
• Data Access− ByteIO− Data Access & Integration
(DAI)
• Data Transfer− Data Movement Interface
Specification (DMIS)− Protocols (e.g. GridFTP)
• Data Transfer− Data Movement Interface
Specification (DMIS)− Protocols (e.g. GridFTP)
• Replica management
• Metadata catalog
• Cache management
• Replica management
• Metadata catalog
• Cache management
Hiro Kishimoto: Keynote GGF17
TICER Summer School, August 24th 2006 62
TICER Summer School, August 24th 2006 63
The State of the ArtThe State of the Art
• Many successful Grid & E-Science projects– A few examples shown in this talk
• Many Grid systems– All largely incompatible– Interoperation talks under way
• Standardisation efforts– Mainly via the Open Grid Forum– A merger of the GGF & EGA
• Significant user investment required– Few “out of the box” solutions
TICER Summer School, August 24th 2006 64
Technical ChallengesTechnical Challenges
• Issues you can’t avoid– Lack of Complete Knowledge (LOCK)– Latency– Heterogeneity– Autonomy– Unreliability– Scalability– Change
• A Challenging goal– balance technical feasibility– against virtual homogeneity, stability and reliability– while remaining affordable, manageable and maintainable
TICER Summer School, August 24th 2006 65
Areas “In Development”Areas “In Development”
• Data provenance• Quality of Service
– Service Level Agreements
• Resource brokering– Across all resources
• Workflow scheduling– Co-sheduling
• Licence management• Software provisioning
– Deployment and update
• Other areas too!
TICER Summer School, August 24th 2006 66
Operational ChallengesOperational Challenges
• Management of distributed systems– With local autonomy
• Deployment, testing & monitoring• User training• User support• Rollout of upgrades• Security
– Distributed identity management– Authorisation– Revocation– Incident response
TICER Summer School, August 24th 2006 67
Grids as a Foundation for SolutionsGrids as a Foundation for Solutions
• The grid per se doesn’t provide– Supported e-Science methods– Supported data & information resources– Computations – Convenient access
• Grids help providers of these, via– International & national secure e-Infrastructure– Standards for interoperation– Standard APIs to promote re-use
• But Research Support must be built– Application developers– Resource providers
TICER Summer School, August 24th 2006 68
Collaboration ChallengesCollaboration Challenges
• Defining common goals
• Defining common formats– E.g. schemas for data and metadata
• Defining a common vocabulary– E.g. for metadata
• Finding common technology– Standards should help, eventually
• Collecting metadata– Automate where possible
TICER Summer School, August 24th 2006 69
Social ChallengesSocial Challenges
• Changing cultures– Rewarding data & resource sharing– Require publication of data
• Taking the first steps– If everyone shares, everyone wins– The first people to share must not lose out
• Sustainable funding– Technology must persist– Data must persist
TICER Summer School, August 24th 2006 70
TICER Summer School, August 24th 2006 71
SummarySummary
• E-Science exploits distributed computing resource to enable new discoveries, new collaborations and new ways of working
• Grid is an enabling technology for e-science.
• Many successful projects exist
• Many challenges remain
TICER Summer School, August 24th 2006 72
Globus Alliance
CeSC (Cambridge)
DigitalCurationCentre
e-Science Institute
UK e-ScienceUK e-Science
EGEE, ChinaGri
d
Grid Operations
SupportCentre
NationalCentre fore-SocialScience
National Institute
forEnvironmental
e-Science
OpenMiddleware
InfrastructureInstitute
TICER Summer School, August 24th 2006 73
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