1. Remarks on Grids e-Science CyberInfrastructure and Peer-to-Peer Networks Los Alamos September 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 (t comm /t calc ~ 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 Name of Grid TypeDescription of Grid Functionality Compute/File Grid or Data File Grid Run multiple jobs with distributed compute and data resources (Global “UNIX Shell”) Desktop Grid e.g. SETI@Home “Internet Computing” and “Cycle Scavenging” with secure sandbox on large numbers of untrusted computers Information Grid or Data Service Grid Grid service access to distributed information, data and knowledge repositories Complexity or Hybrid Grid Hybrid combination of Information and Compute/File Grid emphasizing integration of experimental data, filters and simulations: Data assimilation Campus Grid Grid supporting University community computing Enterprise Grid Grid supporting a company’s enterprise infrastructure

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

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 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 SecurityCatalog Payment Credit Card Warehouse shipping WSDL interfaces

13. 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 WSIL WSFL WSDL SOAP or RMI HTTP or SMTP or IIOP or RMTP TCP/IP Physical Network

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 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 Sensor Data as a Web service (WS) Data Analysis WS Sensor Management WS Visualization WS Simulation WS Filter1 WS Filter2 WS Filter3 WS Build as multiple Filter Web Services Prog1 WS Prog2 WS Build as multiple interdisciplinary Programs Data Analysis WS Simulation WS Visualization WS

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 and Control for DoD Federation of Information systems and modeling and simulation Problem Solving Environment and Software Integration

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

19. 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: data replication, workflow, etc., etc. Domain- specific services Network OGSA Environment Possibly OGSA Not OGSA Given to us from on high

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 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 Microsoft http://msdn.microsoft.com/webservices/default.aspx?pull=/library/en-us/dnglobspec/html/ws-addressing.asp http://msdn.microsoft.com/webservices/default.aspx?pull=/library/en-us/dnglobspec/html/ws-addressing.asp FACTORYFACTORY 1 2 3 4 FACTORYFACTORY 1 2 3 4 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. 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 Execution4: Job Submittal Remote Grid Service 6: File and Storage Access 3: Access to Remote Computers Data 7: Cache Data Replicas 5: Data Transfer 10: Job Status 8: Virtual Data 9: Grid MPI

26. Taxonomy of Grid Operational Style Name of Grid StyleDescription of Grid Operational or Architectural Style Semantic GridIntegration of Grid and Semantic Web meta-data and ontology technologies Peer-to-peer GridGrid built with peer-to-peer mechanisms Lightweight GridGrid designed for rapid deployment and minimum life-cycle support costs Collaboration GridGrid supporting collaborative tools like the Access Grid, whiteboard and shared applications. RRR or Autonomic Grid Fault tolerant and self-healing Grid Robust Reliable Resilient RRR

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, multiple catalogs 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. Database SDE1 SDE2 Service Individual Services Information Ports Grid or Domain Specific Metadata Catalogs System or Federated Registry or Metadata Catalog Database1Database2Database3 Three Metadata Architectures

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

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. 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 Typical codes WS linking to user and Other WS (data sources) Application WS

33. 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 DB Filter WSDL Of Filter OGSA-DAI Interface

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

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

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 (N obs 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 HPC Machine Distributed Machine Data Filter N obs local patch 1 N filtered local patch 1 Data Filter N obs local patch 2 N filtered local patch 2 Geographically Distributed Sensor patches N obs local patch >> N filtered local patch ≈ Number_of_Unknowns local patch Send needed Filter Receive filtered data In simplest approach, filtered data gotten by linear transformations on original data based on Singular Value Decomposition of Least squares matrix Factorize Matrix to product of local patches

38. 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

39. 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 Service1Service2 Service3Service4

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

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