1 overview of e-science and the grid geoffrey fox professor of computer science, informatics,...
TRANSCRIPT
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Overview of e-Science and the Grid
Geoffrey FoxProfessor of Computer Science, Informatics, Physics
Pervasive Technology Laboratories
Indiana University Bloomington IN 47401
December 8 2003
http://www.infomall.org
http://www.grid2002.org
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Grid Computing: Making The Global Infrastructure a Reality
Based on work done in preparing book edited withFran Berman andAnthony J.G. Hey,
ISBN: 0-470-85319-0 Hardcover 1080 Pages Published March 2003 http://www.grid2002.org
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Next Steps Wednesday December 9 Talk – Marlon Pierce on core
Web and Grid Services Technology Next Semester – course on “e-Science and the Grid”
given by Access Grid• Need to decide level and times
A shorter version of this talk was webcast in an Oracle technology serieshttp://webevents.broadcast.com/techtarget/Oracle/100303/index.asp?loc=10
This presentation is at http://grids.ucs.indiana.edu/ptliupages/presentations
See also the “Gap Analysis”http://grids.ucs.indiana.edu/ptliupages/publications/GapAnalysis30June03v2.pdf
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e-Business e-Science and the Grid e-Business captures an emerging view of corporations as
dynamic virtual organizations linking employees, customers and stakeholders across the world. • The growing use of outsourcing is one example
e-Science is the similar vision for scientific research with international participation in large accelerators, satellites or distributed gene analyses.
The Grid integrates the best of the Web, traditional enterprise software, high performance computing and Peer-to-peer systems to provide the information technology infrastructure for e-moreorlessanything.
A deluge of data of unprecedented and inevitable size must be managed and understood.
People, computers, data and instruments must be linked. On demand assignment of experts, computers, networks and
storage resources must be supported
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So what is a Grid? Supporting human decision making with a network of at least
four large computers, perhaps six or eight small computers, and a great assortment of disc files and magnetic tape units - not to mention remote consoles and teletype stations - all churning away. (Licklider 1960)
Coordinated resource sharing and problem solving in dynamic multi-institutional virtual organizations
Infrastructure that will provide us with the ability to dynamically link together resources as an ensemble to support the execution of large-scale, resource-intensive, and distributed applications.
Realizing thirty year dream of science fiction writers that have spun yarns featuring worldwide networks of interconnected computers that behave as a single entity.
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What is a High Performance Computer? We might wish to consider three classes of multi-node computers 1) Classic MPP with microsecond latency and scalable internode
bandwidth (tcomm/tcalc ~ 10 or so) 2) Classic Cluster which can vary from configurations like 1) to 3)
but typically have millisecond latency and modest bandwidth 3) Classic Grid or distributed systems of computers around the
network• Latencies of inter-node communication – 100’s of milliseconds
but can have good bandwidth All have same peak CPU performance but synchronization costs
increase as one goes from 1) to 3) Cost of system (dollars per gigaflop) decreases by factors of 2 at
each step from 1) to 2) to 3) One should NOT use classic MPP if class 2) or 3) suffices unless
some security or data issues dominates over cost-performance One should not use a Grid as a true parallel computer – it can
link parallel computers together for convenient access etc.
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e-Science e-Science is about global collaboration in key areas of
science, and the next generation of infrastructure that will enable it. This is a major UK Program
e-Science reflects growing importance of international laboratories, satellites and sensors and their integrated analysis by distributed teams
CyberInfrastructure is the analogous US initiative
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IMAGING INSTRUMENTS
COMPUTATIONALRESOURCES
LARGE-SCALE DATABASES
DATA ACQUISITION ,ANALYSIS
ADVANCEDVISUALIZATION
Grid Technology supports e-Science and CyberInfrastructure
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Global Terabit Research Network
The Grid software and resources run on top of high performance global networks
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Resources-on-demand Computing-on-demand uses dynamically assigned
(shared) pool of resources to support excess demand in flexible cost-effective fashion
Program AComputer
1
Program ZComputer
26
Program AComputer 27
Program ZComputer
52
Spares
PoolComputer
1
PoolComputer N
<52
Program A
Program Z
Static Assignment with redundancy
Dynamic on-demand Assignment
1010
e-Business and (Virtual) Organizations Enterprise Grid supports information system for an
organization; includes “university computer center”, “(digital) library”, sales, marketing, manufacturing …
Outsourcing Grid links different parts of an enterprise together (Gridsourcing)• Manufacturing plants with designers• Animators with electronic game or film designers and
producers• Coaches with aspiring players (e-NCAA or e-NFL etc.)
Customer Grid links businesses and their customers as in many web sites such as amazon.com
e-Multimedia can use secure peer-to-peer Grids to link creators, distributors and consumers of digital music, games and films respecting rights
Distance education Grid links teacher at one place, students all over the place, mentors and graders; shared curriculum, homework, live classes …
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e-Defense and e-Crisis Grids support Command and Control and provide
Global Situational Awareness • Link commanders and frontline troops to themselves and to
archival and real-time data; link to what-if simulations • Dynamic heterogeneous wired and wireless networks• Security and fault tolerance essential
System of Systems; Grid of Grids• The command and information infrastructure of each ship is
a Grid; each fleet is linked together by a Grid; the President is informed by and informs the national defense Grid
• Grids must be heterogeneous and federated Crisis Management and Response enabled by a Grid
linking sensors, disaster managers, and first responders with decision support
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Classes of Computing Grid Applications Running “Pleasing Parallel Jobs” as in United Devices,
Entropia (Desktop Grid) “cycle stealing systems” Can be managed (“inside” the enterprise as in Condor)
or more informal (as in SETI@Home) Computing-on-demand in Industry where jobs spawned
are perhaps very large (SAP, Oracle …) Support distributed file systems as in Legion (Avaki),
Globus with (web-enhanced) UNIX programming paradigm• Particle Physics will run some 30,000 simultaneous jobs this
way Pipelined applications linking data/instruments,
compute, visualization Seamless Access where Grid portals allow one to choose
one of multiple resources with a common interfaces
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Some Important Styles of Grids Computational Grids were origin of concepts and link
computers across the globe – high latency stops this from being used as parallel machine
Knowledge and Information Grids link sensors and information repositories as in Virtual Observatories or BioInformatics
• More detail on next slide Education Grids link teachers, learners, parents as a VO with
learning tools, distant lectures etc. e-Science Grids link multidisciplinary researchers across
laboratories and universities Community Grids focus on Grids involving large numbers of
peers rather than focusing on linking major resources – links Grid and Peer-to-peer network concepts
Semantic Grid links Grid, and AI community with Semantic web (ontology/meta-data enriched resources) and Agent concepts
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Information/Knowledge Grids Distributed (10’s to 1000’s) of data sources (instruments,
file systems, curated databases …) Data Deluge: 1 (now) to 100’s petabytes/year (2012)
• Moore’s law for Sensors Possible filters assigned dynamically (on-demand)
• Run image processing algorithm on telescope image• Run Gene sequencing algorithm on compiled data
Needs decision support front end with “what-if” simulations
Metadata (provenance) critical to annotate data
Integrate across experiments as in multi-wavelength astronomy
Data Deluge comes from pixels/year available
15152.4 Petabytes Today
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Database Database
Closely Coupled Compute Nodes
Analysis and Visualization
RepositoriesFederated Databases
Sensor Nets Streaming Data
Loosely Coupled Filters
SERVOGrid for e-Geoscience
?DiscoveryServices
SERVOGrid – Solid Earth Research Virtual Observatory will link Australia, Japan, USA ……
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SERVOGrid Requirements Seamless Access to Data repositories and large scale
computers Integration of multiple data sources including sensors,
databases, file systems with analysis system• Including filtered OGSA-DAI (Grid database access)
Rich meta-data generation and access with SERVOGrid specific Schema extending openGIS (Geography as a Web service) standards and using Semantic Grid
Portals with component model for user interfaces and web control of all capabilities
Collaboration to support world-wide work Basic Grid tools: workflow and notification
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In flight data
Airline
Maintenance Centre
Ground Station
Global NetworkSuch as SITA
Internet, e-mail, pager
Engine Health (Data) Center
DAME
Rolls Royce and UK e-Science ProgramDistributed Aircraft Maintenance Environment
~ Gigabyte per aircraft perEngine per transatlantic flight
~5000 engines
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NASA Aerospace Engineering Grid
•Lift Capabilities•Drag Capabilities•Responsiveness
•Deflection capabilities•Responsiveness
•Thrust performance•Reverse Thrust performance•Responsiveness•Fuel Consumption
•Braking performance•Steering capabilities•Traction•Dampening capabilities
Crew Capabilities- accuracy- perception- stamina- re-action times- SOP’s
Engine Models
Airframe Models
Wing Models
Landing Gear Models
Stabilizer Models
Human Models
Whole system simulations are produced by couplingall of the sub-system simulations
It takes a distributed virtual organization to design, simulate and build a complex system like an aircraft
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Virtual Observatory Astronomy GridIntegrate Experiments
Radio Far-Infrared Visible
Visible + X-ray
Dust Map
Galaxy Density Map
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e-Chemistry LaboratoryExperiments-on-demand
X-Raye-Lab
Analysis
Properties
Propertiese-Lab
SimulationVideo
Diffr
acto
mete
r
Globus
StructuresDatabase
Grid Resources
Grid-enabled Output Streams
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CERN LHC Data Analysis Grid
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Raw (HPC) Resources
Middleware
Database
PortalServices
SystemServices
SystemServices
SystemServices
Application Service
SystemServices
SystemServices
UserServices
“Core”Grid
Typical Grid Architecture
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Sources of Grid Technology Grids support distributed collaboratories or virtual
organizations integrating concepts from The Web Agents Distributed Objects (CORBA Java/Jini COM) Globus, Legion, Condor, NetSolve, Ninf and other High
Performance Computing activities Peer-to-peer Networks With perhaps the Web and P2P networks being the most
important for “Information Grids” and Globus for “Compute Grids”
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The Essence of Grid Technology? We will start from the Web view and assert that basic
paradigm is Meta-data rich Web Services communicating via
messages These have some basic support from some runtime
such as .NET, Jini (pure Java), Apache Tomcat+Axis (Web Service toolkit), Enterprise JavaBeans, WebSphere (IBM) or GT3 (Globus Toolkit 3)• These are the distributed equivalent of operating system
functions as in UNIX Shell
• Called Hosting Environment or platform W3C standard WSDL defines IDL (Interface
standard) for Web Services
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Meta-data Meta-data is usually thought of as “data about data” The Semantic Web is at its simplest considered as
adding meta-data to web pages For example, the hospital web-page has meta-data
telling you its location, phone-number, specialties which can be used to automate Google-style searches to allow planning of disease/accident treatment from web
Modern trend (Semantic Grid) is meta-data about web-services e.g. specify details of interface and useage• Such as that a bioinformatics service is free or bandwidth
input is of limited amount Provenance – history and ownership – of data very
important
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A typical Web Service In principle, services can be in any language (Fortran .. Java ..
Perl .. Python) and the interfaces can be method calls, Java RMI Messages, CGI Web invocations, totally compiled away (inlining)
The simplest implementations involve XML messages (SOAP) and programs written in net friendly languages like Java and Python
PaymentCredit Card
WarehouseShippingcontrol
WSDL interfaces
WSDL interfaces
Security CatalogPortalService
Web Services
Web Services
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Services and Distributed Objects A web service is a computer program running on either the local
or remote machine with a set of well defined interfaces (ports) specified in XML (WSDL)
Web Services (WS) have many similarities with Distributed Object (DO) technology but there are some (important) technical and religious points (not easy to distinguish)• CORBA Java COM are typical DO technologies• Agents are typically SOA (Service Oriented Architecture)
Both involve distributed entities but Web Services are more loosely coupled• WS interact with messages; DO with RPC (Remote Procedure Call)• DO have “factories”; WS manage instances internally and interaction-
specific state not exposed and hence need not be managed• DO have explicit state (statefull services); WS use context in the messages
to link interactions (statefull interactions) Claim: DO’s do NOT scale; WS build on experience (with
CORBA) and do scale
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Details of Web Service Protocol Stack UDDI finds where programs are
• remote (distributed) programs are just Web Services
• (not a great success) WSFL links programs together
(under revision as BPEL4WS) WSDL defines interface (methods,
parameters, data formats) SOAP defines structure of message
including serialization of information HTTP is negotiation/transport protocol TCP/IP is layers 3-4 of OSI Physical Network is layer 1 of OSI
UDDI or WSILUDDI or WSIL
WSFLWSFL
WSDLWSDL
SOAP or RMISOAP or RMI
HTTP or SMTP or IIOP or
RMTP
HTTP or SMTP or IIOP or
RMTP
TCP/IPTCP/IP
Physical Network
Physical Network
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Classic Grid Architecture
Database Database
Netsolve
Computing
SecurityCollaboration
CompositionContent Access
Resources
Clients Users and Devices
Middle TierBrokers Service Providers
Middle Tier becomes Web Services
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Grid Services for the Education Process “Learning Object” XML standards already exist Registration Performance (grading) Authoring of Curriculum Online laboratories for real and virtual instruments Homework submission Quizzes of various types (multiple choice, random parameters) Assessment data access and analysis Synchronous Delivery of Curricula including Audio/Video
Conferencing and other synchronous collaborative tools as Web Services
Scheduling of courses and mentoring sessions Asynchronous access, data-mining and knowledge discovery Learning Plan agents to guide students and teachers
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Grid Learning Model Education and Research Grids share some services
both for content and “process”• For example collaboration services are largely identical
• Research will use much larger simulation engines to get high resolution results
• Maybe a researcher uses a CAVE to visualize; education a Macintosh
But both can share data services but run through different filters to select for precision (research) or pedagogical value (education)
Education has “digital textbook” frontend to resources of the research Grid
Both use same workflow technologies to link services together
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Database Database
Coarse grain simulations
Analysis and Visualization
RepositoriesFederated Databases
Field Trip Data Streaming Data
Loosely Coupled Filters
Sensors
?DiscoveryServices
SERVOGrid for e-Education
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Implementing Grids for Education I Need to design a service architecture for education
• Build on services from broader fields
• Need some specific EducationML specifying services and properties
Note IMS (http://www.imsproject.org/) and ADL have a lot of education property metadata but no services
• Need more use of standards outside education but much of IMS can be used
Use services where-ever possible but only if “coarse-grain”
Module A
Module B
Method Calls.001 to 1 millisecond
Service A
Service B
Messages
0.1 to 1000 millisecond latency
Coarse Grain Service ModelClosely coupled Java/Python …
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Implementing Grids for Education II Build a Education Grid prototype addressing content and
process• Focus education grid on a curriculum area (using Grids!)
such as Geoscience or even e-Science/Information Technology/Science Informatics
Re-use Grid services in systems area (portals, security, collaboration ..) and from application domain• What research Grid services can be re-used; what need to be
significantly changed or customized• Develop some “Education process” services
Supply leadership in use of CyberInfrastructure/Grids in education• Feed Education needs to CyberInfrastructure and vice-versa
Perform a requirement analysis analogous to Gap Analysishttp://grids.ucs.indiana.edu/ptliupages/publications/GapAnalysis30June03v2.pdf
Develop curriculum in Grids, e-Science and CyberInfrastructure
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Some Observations “Traditional “ Grids manage and share asynchronous resources in
a rather centralized fashion Peer-to-peer networks are “just like” Grids with different
implementations of message-based services like registration and look-up
Collaboration systems like WebEx/Placeware (Application sharing) or Polycom (audio/video conferencing) can be viewed as Grids
Computers are fast and getting faster. One can afford many strategies that used to be unrealistic including rich usually XML based messaging
Web Services interact with messages
• Everything (including applications like PowerPoint) will be a Web Service?
• Grids, P2P Networks, Collaborative Environments are (will be) managed message-linked Web Services
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Peer to Peer Grid
DatabaseDatabase
Peers
Peers
Peer to Peer GridA democratic organization
User FacingWeb Service Interfaces
Service FacingWeb Service Interfaces
Event/MessageBrokers
Event/MessageBrokers
Event/MessageBrokers
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System and Application Services? There are generic Grid system services: security, collaboration,
persistent storage, universal access• OGSA (Open Grid Service Architecture) is implementing these
as extended Web Services An Application Web Service is a capability used either by another
service or by a user• It has input and output ports – data is from sensors or other
services Consider Satellite-based Sensor Operations as a Web Service
• Satellite management (with a web front end)• Each tracking station is a service• Image Processing is a pipeline of filters – which can be
grouped into different services• Data storage is an important system service• Big services built hierarchically from “basic” services
Portals are the user (web browser) interfaces to Web services
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Satellite Science Grid Environment
Sensor Data as a Web
service (WS)
Data Analysis WS
Sensor Management
WS
Visualization WS
Simulation WS
Filter1WS
Filter2WS
Filter3WS
Build as multiple Filter Web Services
Prog1WS
Prog2WS
Build as multiple interdisciplinaryPrograms
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What is Happening? Grid ideas are being developed in (at least) two
communities• Web Service – W3C, OASIS• Grid Forum (High Performance Computing, e-Science)
Service Standards are being debated Grid Operational Infrastructure is being deployed Grid Architecture and core software being developed Particular System Services are being developed
“centrally” – OGSA framework for this in Lots of fields are setting domain specific standards and
building domain specific services There is a lot of hype Grids are viewed differently in different areas
• Largely “computing-on-demand” in industry (IBM, Oracle, HP, Sun)
• Largely distributed collaboratories in academia
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OGSA OGSI & Hosting Environments Start with Web Services in a hosting environment Add OGSI to get a Grid service and a component model Add OGSA to get Interoperable Grid “correcting” differences in base platform
and adding key functionalities
OGSI on Web Services
Broadly applicable services: registry,authorization, monitoring, data
access, etc., etc.
Hosting Environment for WS
More specialized services: datareplication, workflow, etc., etc.
Domain -specific services
Network
OGSAEnvironment
Possibly OGSA
Not OGSA
Given to us from on high
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Technical Activities of Note Look at different styles of Grids such as Autonomic (Robust
Reliable Resilient) New Grid architectures hard due to investment required Critical Services Such as
• Security – build message based not connection based• Notification – event services• Metadata – Use Semantic Web, provenance• Databases and repositories – instruments, sensors• Computing – Submit job, scheduling, distributed file
systems• Visualization, Computational Steering• Fabric and Service Management• Network performance
Program the Grid – Workflow Access the Grid – Portals, Grid Computing Environments
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Issues and Types of Grid Services 1) Types of Grid
• R3• Lightweight• P2P• Federation and Interoperability
2) Core Infrastructure and Hosting Environment
• Service Management• Component Model• Service wrapper/Invocation • Messaging
3) Security Services• Certificate Authority• Authentication• Authorization• Policy
4) Workflow Services and Programming Model
• Enactment Engines (Runtime)• Languages and Programming• Compiler• Composition/Development
5) Notification Services 6) Metadata and Information Services
• Basic including Registry• Semantically rich Services and meta-
data• Information Aggregation (events)• Provenance
7) Information Grid Services• OGSA-DAI/DAIT• Integration with compute resources• P2P and database models
8) Compute/File Grid Services• Job Submission• Job Planning Scheduling
Management• Access to Remote Files, Storage and
Computers• Replica (cache) Management• Virtual Data• Parallel Computing
9) Other services including• Grid Shell• Accounting• Fabric Management• Visualization Data-mining and
Computational Steering• Collaboration
10) Portals and Problem Solving Environments
11) Network Services• Performance• Reservation• Operations
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Data
Technology Components of (Services in)a Computing Grid
1: Job Management Service(Grid Service Interface to user or program client)
2: Schedule and control Execution
1: Plan Execution 4: Job Submittal
Remote Grid ServiceRemote Grid Service
6: File andStorage Access
3: Access to Remote Computers
Data
7: CacheData
Replicas5: Data Transfer
10: JobStatus
8: VirtualData
9: Grid MPI
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Approach Build on e-Science methodology and Grid
technology Science applications with multi-scale models,
scalable parallelism, data assimilation as key issues• Data-driven models for earthquakes,
climate, environment ….. Use existing code/database technology
(SQL/Fortran/C++) linked to “Application Web/OGSA services” • XML specification of models,
computational steering, scale supported at “Web Service” level as don’t need “high performance” here
• Allows use of Semantic Grid technology
Typicalcodes
WS linkingto user andOther WS
(data sources)
Application WS
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Raw (HPC) Resources
Middleware
Database
PortalServices
SystemServices
SystemServices
SystemServices
Application Service
SystemServices
SystemServices
GridComputing
Environments
UserServices
“Core”Grid
Application Metadata
Actual Application
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Why we can dream of using HTTP and that slow stuff
We have at least three tiers in computing environment Client (user portal) “Middle Tier” (Web Servers/brokers) Back end (databases, files, computers etc.) In Grid programming, we use HTTP (and used to use
CORBA and Java RMI) in middle tier ONLY to manipulate a proxy for real job• Proxy holds metadata • Control communication in middle tier only uses metadata• “Real” (data transfer) high performance communication in
back end
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Virtualization The Grid could and sometimes does virtualize
various concepts – should do more Location: URI (Universal Resource Identifier)
virtualizes URL (WSAddressing goes further) Replica management (caching) virtualizes file
location generalized by GriPhyn virtual data concept Protocol: message transport and WSDL bindings
virtualize transport protocol as a QoS request P2P or Publish-subscribe messaging virtualizes
matching of source and destination services Semantic Grid virtualizes Knowledge as a meta-data
query Brokering virtualizes resource allocation Virtualization implies all references can be indirect
and needs powerful mapping (look-up) services -- metadata
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Integration of Data and Filters One has the OGSA-DAI Data repository interface combined
with WSDL of the (Perl, Fortran, Python …) filter User only sees WSDL not data syntax Some non-trivial issues as to where the filtering compute
power is• Microsoft says filter next to data
DBFilter
WSDL
Of Filter
OGSA-DAI
Interface
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DatabaseService
SensorService
ComputeService
ParallelSimulation
Service
Middle Tier with XML Interfaces
VisualizationService
ApplicationService-1
Users
Database
ApplicationService-2
ApplicationService-3
CCE Control Portal Aggregation
SERVOGrid Complexity Computing Environment
XML Meta-dataService
ComplexitySimulation
Service
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HPCSimulation
DataFilter
Data FilterD
ata
Filt
er
Data
Filter
Data
Filter
Distributed Filters massage dataFor simulation
Other
Grid
and W
eb
Servi
ces
AnalysisControl
Visualize
SERVOGrid (Complexity) Computing Model
Grid
OGSA-DAIGrid Services
This Type of Gridintegrates with
Parallel computingMultiple HPC
facilities but only use one at a time
Many simultaneous data sources and
sinks
Grid Data Assimilation
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Two-level Programming I The paradigm implicitly assumes a two-level
Programming Model We make a Service (same as a “distributed object” or
“computer program” running on a remote computer) using conventional technologies• C++ Java or Fortran Monte Carlo module• Data streaming from a sensor or Satellite• Specialized (JDBC) database access
Such services accept and produce data from users files and databases
The Grid is built by coordinating such services assuming we have solved problem of programming the service
Service Data
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Two-level Programming II The Grid is discussing the composition of distributed
services with the runtime interfaces to Grid as opposed to UNIX pipes/data streams
Familiar from use of UNIX Shell, PERL or Python scripts to produce real applications from core programs
Such interpretative environments are the single processor analog of Grid Programming
Some projects like GrADS from Rice University are looking at integration between service and composition levels but dominant effort looks at each level separately
Service1 Service2
Service3 Service4
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Conclusions Grids are inevitable and pervasive Can expect Web Services and Grids to merge with a common
set of general principles but different implementations with different scaling and functionality trade-offs
e-Science will grow in importance as Science grows as an international “team sport”; affects scientists and organizations
Enough is known that one can start today We will be flooded with data, information and purported
knowledge One should be learning about Grids; understanding relevant
Web and Grid standards and developing new domain specific standards
Note many existing (standards) efforts assume client-server and not a brokered service model; these will need to change!