Geoffrey Fox Community Grids Lab Indiana University gcf@indiana

1. Remarks onGrids e-ScienceCyberInfrastructureand Peer-to-PeerNetworksLos AlamosSeptember 23 2003 Geoffrey Fox Community Grids Lab Indiana University gcf@indiana.edu

2. What is 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.

3. What is a Grid I? • Collaborative Environment (Ch2.2,18) • Combining powerful resources, federated computing and a security structure (Ch38.2) • Coordinated resource sharing and problem solving in dynamic multi-institutional virtual organizations (Ch6) • Data Grids as Managed Distributed Systems for Global Virtual Organizations (Ch39) • Distributed Computing or distributed systems (Ch2.2,10) • Enabling Scalable Virtual Organizations (Ch6) • Enabling use of enterprise-wide systems, and someday nationwide systems, that consist of workstations, vector supercomputers, and parallel supercomputers connected by local and wide area networks. Users will be presented the illusion of a single, very powerful computer, rather than a collection of disparate machines. The system will schedule application components on processors, manage data transfer, and provide communication and synchronization in such a manner as to dramatically improve application performance. Further, boundaries between computers will be invisible, as will the location of data and the failure of processors. (Ch10)

4. What is a Grid II? • Supporting e-Science representing increasing global collaborations of people and of shared resources that will be needed to solve the new problems of Science and Engineering (Ch36) • As 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. (Ch1) • Makes high-performance computers superfluous (Ch6) • Metasystems or metacomputing systems (Ch10,37) • Middleware as the services needed to support a common set of applications in a distributed network environment (Ch6) • Next Generation Internet (Ch6) • Peer-to-peer Network (Ch10, 18) • Realizing thirty year dream of science fiction writers that have spun yarns featuring worldwide networks of interconnected computers that behave as a single entity. (Ch10) • Technology on which to build CyberInfrastructure (NSF) • High Performance Computing World’s view of the Web The Grid for my purposes is “best practice” in all of this!

5. Taxonomy of Grid Functionalities

6. 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

7. Information/Knowledge Grids • These are typified by virtual observatory and bioinformatics applications • Distributed (10’s to 1000’s) of data sources (instruments, file systems, curated databases …) • Possible filters assigned dynamically • Run image processing algorithm on telescope image • Run Gene sequencing algorithm on data from EBI/NCBI • Integrate across experiments as in multi-wavelength astronomy • Needs decision support front end with “what-if” simulations • Metadata (provenance) critical to annotate data • SERVOGrid – Solid Earth Research Virtual Observatory will link Japan, Australia, USA

8. SERVOGrid Caricature RepositoriesFederated Databases Sensor Nets Streaming Data Database Database Analysis and Visualization Loosely Coupled Filters Closely Coupled Compute Nodes

9. 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”

10. 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

11. 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 • 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 • 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

12. A typical Web Service PaymentCredit Card WSDL interfaces Security Catalog Warehouse shipping WSDL interfaces • 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

13. Details of Web Service Protocol Stack UDDI or WSIL WSFL WSDL SOAP or RMI HTTP or SMTP or IIOP or RMTP TCP/IP Physical Network • 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

14. What are 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

15. Application Web Services Prog1WS Prog2WS Filter1WS Filter2WS Filter3WS Build as multiple interdisciplinaryPrograms Build as multiple Filter Web Services Sensor Data as a Webservice (WS) Simulation WS Simulation WS Data Analysis WS Data Analysis WS Sensor ManagementWS Visualization WS Visualization WS • Note Service model integrates sensors, sensor analysis, simulations and people • 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 users, sensors or other services • Big services built hierarchically from “basic” services

16. 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

17. Grid Applications • Cope with Data Deluge – Moore’s law for detectors • Astronomy – virtual observatories • Biology – distributed repositories and filtering • Chemistry – online laboratories • Earth/Environmental Science – distributed sensors • Engineering – distributed monitors • Health – medical instruments and images • Particle Physics – analyze LHC data • Gridsourcing – animation in China, software in India and design/leadership in USA • Basketball coaching in Indiana, players in China • Teachers in Los Alamos, students in universities • Command andControl for DoD • Federation of Information systems and modeling and simulation • Problem Solving Environment and Software Integration

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

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

20. OGSI Open Grid Service Interface • http://www.gridforum.org/ogsi-wg • It is a “component model” for web services. • It defines a set of behavior patterns that each OGSI service must exhibit. • Every “Grid Service” portType extends a common base type. • Defines an introspection model for the service • You can query it (in a standard way) to discover • What methods/messages a port understands • What other port types does the service provide? • If the service is “stateful” what is the current state? • Factory Model • A set of standard portTypes for • Message subscription and notification • Service collections • Each service is identified by a URI called the “Grid Service Handle” • GSHs are bound dynamically to Grid Services References (typically wsdl docs) • A GSR may be transient. GSHs are fixed. • Handle map services translate GSHs into GSRs.

21. OGSI and Stateful Services • Sometimes you can send a message to a service, get a result and that’s the end • This is a statefree service • However most non-trivial services need state to allow persistent asynchronous interactions • OGSI is designed to support Stateful services through two mechanisms • Information Port: where you can query for SDE (Service Definition Elements) • “Factories” that allow one to view a Service as a “class” (in an object-oriented language sense) and create separate instances for each Service invocation • There are several interesting issues here • Difference between Stateful interactions and Stateful services • System or Service managed instances

22. Factories and OGSI 1 1 F A C T O R Y F A C T O R Y 2 2 3 3 4 4 • Stateful interactions are typified by amazon.com where messages carry correlation information allowing multiple messages to be linked together • Amazon preserves state in this fashion which is in fact preserved in its database permanently • Stateful services have state that can be queried outside a particular interaction • Also note difference between implicit and explicit factories • Some claim that implicit factories scale as each service manages its own instances and so do not need to worry about registering instances and lifetime management • See WS-Addressing from largely IBM and Microsofthttp://msdn.microsoft.com/webservices/default.aspx?pull=/library/en-us/dnglobspec/html/ws-addressing.asp Explicit Factory Implicit Factory

23. 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

24. 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

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

26. Taxonomy of Grid Operational Style

27. 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

28. Metadata and Semantic Grid • Can store in one catalog, multiplecatalogs or in each service • Not clear how a coherent approach will develop • Specialized metadata services like UDDI and MDS (Globus) • Nobody likes UDDI • MDS uses old fashioned LDAP • RGMA is MDS with a relational database backend • Some basic XML database (Oracle, Xindice …) • “By hand” as in current SERVOGrid Portal which is roughly same as using service stored SDE’s (Service Data Elements) as in OGSI • Semantic Web (Darpa) produced a lot of metadata tools aimed at annotating and searching/reasoning about metadata enhanced webpages • Semantic Grid uses for enriching Web Services • Implies interesting programming model with traditional analysis (compiler) augmented by meta-data annotation

29. Three Metadata Architectures System or Federated Registry or Metadata Catalog Grid or Domain Specific Metadata Catalogs Database1 Database2 Database3 Information Ports SDE1SDE2Service SDE1SDE2Service SDE1SDE2Service SDE1SDE2Service SDE1SDE2Service SDE1SDE2Service SDE1SDE2Service Database Individual Services

30. Jobs Tools Database Selected GeoInformatics Data Tool MetaData XML Meta-dataService MultiScale Ontologies Job MetaData Complexity Scripts Workflow SERVOPSE Programs using CCEML(SERVOML) SERVOGrid ComplexitySimulation Service Importance of Metadata Service; how should this be implemented?

31. 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 • Rich meta-data generation and access with SERVOGrid specific Schema extending openGIS 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

32. Application WS Approach WS linking to user and Other WS (data sources) Typicalcodes • 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

33. Integration of Data and Filters WSDL Of Filter OGSA-DAI Interface Filter DB • 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

34. SERVOGrid Complexity Computing Environment Database Parallel SimulationService DatabaseService ComputeService Sensor Service Middle Tier with XML Interfaces ApplicationService-1 XML Meta-dataService ApplicationService-2 CCE Control Portal Aggregation ComplexitySimulationService ApplicationService-3 Users VisualizationService

35. Data Data Filter Filter Filter Data Filter Data OGSA-DAIGrid Services AnalysisControl Visualize Grid Data Filter This Type of Grid integrates with Parallel computing Multiple HPC facilities but only use one at a time Many simultaneous data sources and sinks HPC Simulation Grid Data Assimilation Other Gridand Web Services Distributed Filters massage data For simulation SERVOGrid (Complexity)Computing Model

36. Data Assimilation • Data assimilation implies one is solving some optimization problem which might have Kalman Filter like structure • As discussed by DAO at Earth Science meeting, one will become more and more dominated by the data (Nobs much larger than number of simulation points). • Natural approach is to form for each local (position, time) patch the “important” data combinations so that optimization doesn’t waste time on large error or insensitive data. • Data reduction done in natural distributed fashion NOT on HPC machine as distributed computing most cost effective if calculations essentially independent • Filter functions must be transmitted from HPC machine

37. Distributed Filtering Nobslocal patch >> Nfilteredlocalpatch≈ Number_of_Unknownslocalpatch In simplest approach, filtered data gotten by linear transformations on original data based on Singular Value Decomposition of Least squares matrix Send needed Filter Receive filtered data Nobslocal patch 1 Filter Data Nfilteredlocal patch 1 Geographically DistributedSensor patches Nobslocal patch 2 Filter Data Nfilteredlocal patch 2 HPC Machine Factorize Matrixto product of local patches Distributed Machine

38. Two-level Programming I Service Data • 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

39. Two-level Programming II Service1 Service3 Service2 Service4 • 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

40. 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

41. Portal Services SystemServices SystemServices Application Service Application Metadata Middleware SystemServices SystemServices SystemServices Raw (HPC) Resources Actual Application Database UserServices GridComputingEnvironments “Core”Grid

42. Workflow and SERVOGrid CCE • SERVOGrid will use workflow technology to support both • “code and data coupling” • Multiscale features • Implementing multiscale model requires • building Web services for each model, • describing each model with metadata and • Describing linkage of models (linkage of ports on web services) • And describing when to use which scale model • So workflow and multiscale depend on web services described by rich metadata • This analysis isn’t correct if scales must be “tightly coupled” as current workflow won’t support this (area addressed by CCA from DoE) • We should focus on multiscale models with loose “service” coupling • Hopefully we will learn how to take same architecture, compile away inefficiencies and get high performance on tighter coupling than conventional distributed workflow