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Overview of e-Science and the Grid Geoffrey Fox Professor of Computer Science, Informatics, Physics Overview of e-Science and the Grid Geoffrey Fox Professor of Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN 47401 December 8 2003 [email protected] edu http: //www. infomall. org http: //www. grid 2002. org 1

Grid Computing: Making The Global Infrastructure a Reality n n n Based on work Grid Computing: Making The Global Infrastructure a Reality n n n Based on work done in preparing book edited with Fran Berman and Anthony J. G. Hey, ISBN: 0 -470 -85319 -0 Hardcover 1080 Pages Published March 2003 http: //www. grid 2002. org 2

Next Steps n n Wednesday December 9 Talk – Marlon Pierce on core Web Next Steps n n 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 n A shorter version of this talk was webcast in an Oracle technology series http: //webevents. broadcast. com/techtarget/Oracle/100303/index. asp? loc=10 n n This presentation is at http: //grids. ucs. indiana. edu/ptliupages/presentations See also the “Gap Analysis” http: //grids. ucs. indiana. edu/ptliupages/publications/Gap. Analysis 30 June 03 v 2. pdf 3

e-Business e-Science and the Grid n n n e-Business captures an emerging view of e-Business e-Science and the Grid n n n 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 Peerto-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 4

So what is a Grid? n n Supporting human decision making with a network So what is a Grid? n n 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. 5

What is a High Performance Computer? n n n n We might wish to What is a High Performance Computer? n n n n 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. 6

e-Science n n n e-Science is about global collaboration in key areas of science, e-Science n n n 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 Cyber. Infrastructure is the analogous US initiative Grid Technology supports e-Science and Cyber. Infrastructure 7

Global Terabit Research Network n The Grid software and resources run on top of Global Terabit Research Network n The Grid software and resources run on top of high performance global networks 8

Resources-on-demand n Computing-on-demand uses dynamically assigned (shared) pool of resources to support excess demand Resources-on-demand n Computing-on-demand uses dynamically assigned (shared) pool of resources to support excess demand in flexible cost-effective fashion Program A Computer 1 Program Z Computer 26 Static Assignment with redundancy Program Z Computer 52 Program A Computer 27 Spares Program A Pool Computer 1 Program Z Pool Computer N <52 Dynamic on-demand Assignment 9

e-Business and (Virtual) Organizations n n n Enterprise Grid supports information system for an e-Business and (Virtual) Organizations n n n 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, 10 homework, live classes …

e-Defense and e-Crisis n Grids support Command Control and provide Global Situational Awareness • e-Defense and e-Crisis n Grids support Command 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 n System of Systems; Grid of Grids • The command 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 n Crisis Management and Response enabled by a Grid linking sensors, disaster managers, and first responders with decision support 11

Classes of Computing Grid Applications n n Running “Pleasing Parallel Jobs” as in United Classes of Computing Grid Applications n n 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 [email protected]) 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 n n Pipelined applications linking data/instruments, compute, visualization Seamless Access where Grid portals allow one to choose one of multiple resources with a common interfaces 12

Some Important Styles of Grids n n n Computational Grids were origin of concepts Some Important Styles of Grids n n n 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 Bio. Informatics • 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 13

Information/Knowledge Grids n n Distributed (10’s to 1000’s) of data sources (instruments, file systems, Information/Knowledge Grids n n 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 n n 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 14

2. 4 Petabytes Today 15 2. 4 Petabytes Today 15

Repositories Federated Databases Database Sensor Nets Streaming Database SERVOGrid for e-Geoscience ? Loosely Coupled Repositories Federated Databases Database Sensor Nets Streaming Database SERVOGrid for e-Geoscience ? Loosely Coupled Filters Discovery Services Analysis and Visualization Closely Coupled Compute Nodes SERVOGrid – Solid Earth Research Virtual Observatory will link 16 Australia, Japan, USA ……

SERVOGrid Requirements n n Seamless Access to Data repositories and large scale computers Integration SERVOGrid Requirements n n 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) n n Rich meta-data generation and access with SERVOGrid specific Schema extending open. GIS (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 17

DAME In flight data ~ Gigabyte per aircraft per Engine per transatlantic flight Airline DAME In flight data ~ Gigabyte per aircraft per Engine per transatlantic flight Airline ~5000 engines Global Network Such as SITA Ground Station Engine Health (Data) Center Maintenance Centre Internet, e-mail, pager Rolls Royce and UK e-Science Program Distributed Aircraft Maintenance Environment 18

NASA Aerospace Engineering Grid It takes a distributed virtual organization to design, simulate and NASA Aerospace Engineering Grid It takes a distributed virtual organization to design, simulate and build a complex system like an aircraft 19

Virtual Observatory Astronomy Grid Integrate Experiments Radio Far-Infrared Visible Dust Map Visible + X-ray Virtual Observatory Astronomy Grid Integrate Experiments Radio Far-Infrared Visible Dust Map Visible + X-ray 20 Galaxy Density Map

e-Chemistry Laboratory Experiments-on-demand Grid-enabled Output Streams Grid Resources 21 e-Chemistry Laboratory Experiments-on-demand Grid-enabled Output Streams Grid Resources 21

CERN LHC Data Analysis Grid 22 CERN LHC Data Analysis Grid 22

Typical Grid Architecture Portal Services System Services User Services System Services Application Service Middleware Typical Grid Architecture Portal Services System Services User Services System Services Application Service Middleware System Services “Core” Grid System Services Raw (HPC) Resources Database 23

Sources of Grid Technology n n n n Grids support distributed collaboratories or virtual Sources of Grid Technology n n n n Grids support distributed collaboratories or virtual organizations integrating concepts from The Web Agents Distributed Objects (CORBA Java/Jini COM) Globus, Legion, Condor, Net. Solve, Ninf and other High Performance Computing activities Peer-to-peer Networks With perhaps the Web and P 2 P networks being the most important for “Information Grids” and Globus for “Compute Grids” 24

The Essence of Grid Technology? n n n We will start from the Web The Essence of Grid Technology? n n n 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 Java. Beans, Web. Sphere (IBM) or GT 3 (Globus Toolkit 3) • These are the distributed equivalent of operating system functions as in UNIX Shell • Called Hosting Environment or platform n W 3 C standard WSDL defines IDL (Interface standard) for Web Services 25

Meta-data n n Meta-data is usually thought of as “data about data” The Semantic Meta-data n n 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 webservices e. g. specify details of interface and useage • Such as that a bioinformatics service is free or bandwidth input is of limited amount n Provenance – history and ownership – of data very important 26

A typical Web Service n n In principle, services can be in any language A typical Web Service n n 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 Web Services WSDL interfaces Portal Service Security WSDL interfaces Web Services Payment Credit Card Catalog Warehouse Shipping control 27

Services and Distributed Objects n n A web service is a computer program running Services and Distributed Objects n n 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) n 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 interactionspecific 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) n Claim: DO’s do NOT scale; WS build on experience (with CORBA) and do scale 28

Details of Web Service Protocol Stack n n n n UDDI finds where programs Details of Web Service Protocol Stack n n n n UDDI finds where programs are • remote (distributed) programs are just Web Services • (not a great success) WSFL links programs together (under revision as BPEL 4 WS) 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 WSIL WSFL WSDL SOAP or RMI HTTP or SMTP or IIOP or RMTP TCP/IP Physical Network 29

Classic Grid Architecture Resources Database Composition Content Access Netsolve Security Collaboration Middle Tier Brokers Classic Grid Architecture Resources Database Composition Content Access Netsolve Security Collaboration Middle Tier Brokers Service Providers Computing Middle Tier becomes Web Services Clients Users and Devices 30

Grid Services for the Education Process n n n “Learning Object” XML standards already Grid Services for the Education Process n n n “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 31

Grid Learning Model n Education and Research Grids share some services both for content Grid Learning Model n 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 n n n 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 32

Repositories Federated Databases Database Field Trip Data Sensors Streaming Database SERVOGrid for e-Education ? Repositories Federated Databases Database Field Trip Data Sensors Streaming Database SERVOGrid for e-Education ? Loosely Coupled Filters Discovery Services Analysis and Visualization Coarse grain simulations 33

Implementing Grids for Education I n n n Need to design a service architecture Implementing Grids for Education I n n n Need to design a service architecture for education • Build on services from broader fields • Need some specific Education. ML 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” Closely coupled Java/Python … Module B Module A Method Calls. 001 to 1 millisecond Coarse Grain Service Model Service B Messages Service A 0. 1 to 1000 millisecond latency 34

Implementing Grids for Education II n n Build a Education Grid prototype addressing content Implementing Grids for Education II n n 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 Cyber. Infrastructure/Grids in education • Feed Education needs to Cyber. Infrastructure and vice-versa Perform a requirement analysis analogous to Gap Analysis http: //grids. ucs. indiana. edu/ptliupages/publications/Gap. Analysis 30 June 03 v 2. pdf n Develop curriculum in Grids, e-Science and Cyber. Infrastructure 35

Some Observations n n n “Traditional “ Grids manage and share asynchronous resources in Some Observations n n n “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 Web. Ex/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 Power. Point) will be a Web Service? • Grids, P 2 P Networks, Collaborative Environments are (will 36 be) managed message-linked Web Services

Database Peers Database Service Facing Web Service Interfaces Event/ Message Brokers Peer to Peer Database Peers Database Service Facing Web Service Interfaces Event/ Message Brokers Peer to Peer Grid Peers User Facing Web Service Interfaces A democratic organization 37 Peer to Peer Grid

System and Application Services? n n There are generic Grid system services: security, collaboration, System and Application Services? n n 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 38

Satellite Science Grid Environment 39 Satellite Science Grid Environment 39

What is Happening? n Grid ideas are being developed in (at least) two communities What is Happening? n Grid ideas are being developed in (at least) two communities • Web Service – W 3 C, OASIS • Grid Forum (High Performance Computing, e-Science) n n n n 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 40

OGSA OGSI & Hosting Environments n n n Start with Web Services in a OGSA OGSI & Hosting Environments n n n 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 Not OGSA Domain -specific services Possibly OGSA More specialized services: data replication, workflow, etc. OGSA Environment Broadly applicable services: registry, authorization, monitoring, data access, etc. OGSI on Web Services Given to us from on high Hosting Environment for WS Network 41

Technical Activities of Note n n n Look at different styles of Grids such Technical Activities of Note n n n 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 42

Issues and Types of Grid Services n • • • • n n • Issues and Types of Grid Services n • • • • n n • • 1) Types of Grid R 3 Lightweight P 2 P 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 metadata Information Aggregation (events) Provenance n n n 7) Information Grid Services • OGSA-DAI/DAIT • Integration with compute resources • P 2 P 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 43 • Operations

Remote Grid Service 10: Job Status Remote Grid Service 1: Job Management Service (Grid Remote Grid Service 10: Job Status Remote Grid Service 1: Job Management Service (Grid Service Interface to user or program client) 1: Plan Execution 4: Job Submittal 2: Schedule and control Execution 3: Access to Remote Computers Data 7: Cache Data Replicas 9: Grid MPI 5: Data Transfer 6: File and Storage Access 8: Virtual Data Technology Components of (Services in) a Computing Grid 44

Approach n n n Build on e-Science methodology and Grid technology Science applications with Approach n n n 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 Application WS WS linking to user and Other WS (data sources) Typical codes 45

User Services System Services Grid Computing Environments Portal Services System Services Application Metadata Service User Services System Services Grid Computing Environments Portal Services System Services Application Metadata Service Middleware System Services Actual Application System Services Raw (HPC) Resources “Core” Grid Database 46

Why we can dream of using HTTP and that slow stuff n n n Why we can dream of using HTTP and that slow stuff n n n 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 47 back end

Virtualization n n n n The Grid could and sometimes does virtualize various concepts Virtualization n n n n 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 Gri. Phyn virtual data concept Protocol: message transport and WSDL bindings virtualize transport protocol as a Qo. S request P 2 P 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 48

Integration of Data and Filters n n n One has the OGSA-DAI Data repository Integration of Data and Filters n n n 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 WSDL Of Filter OGSA-DAI Interface DB 49

SERVOGrid Complexity Computing Environment Database Service Application Service-1 Application Service-2 Application Service-3 Parallel Simulation SERVOGrid Complexity Computing Environment Database Service Application Service-1 Application Service-2 Application Service-3 Parallel Simulation Service Compute Service Middle Tier with XML CCE Control Portal Aggregation Users Sensor Service Interfaces XML Meta-data Service Complexity Simulation Service Visualization Service 50

Dat a a Dat Grid F ilter er Filt OGSA-DAI Grid Services Dat a Dat a a Dat Grid F ilter er Filt OGSA-DAI Grid Services Dat a F ilter Grid Data Assimilation HPC Simulation This Type of Grid integrates with Parallel computing Multiple HPC facilities but only use one at a time Many simultaneous data sources and sinks O r Dat a Distributed Filters massage data For simulation an G the Se d rid r rv We ic b es ilter F Filte ata D Analysis Control Visualize SERVOGrid (Complexity) Computing Model 51

Two-level Programming I n n The paradigm implicitly assumes a two-level Programming Model We Two-level Programming I n n 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 n Such services accept and produce data from users files and databases Service n Data The Grid is built by coordinating such services assuming we have solved problem of programming the service 52

Two-level Programming II n n The Grid is discussing the composition of distributed services Two-level Programming II n n The Grid is discussing the composition of distributed services with the runtime Service 1 Service 2 interfaces to Grid as opposed to UNIX Service 3 Service 4 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 Gr. ADS from Rice University are looking at integration between service and composition levels but dominant effort looks at each level separately 53

Conclusions n n n n Grids are inevitable and pervasive Can expect Web Services Conclusions n n n n 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! 54