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NeSCR Dec-3 -2003 Bertram Ludaescher Scientific Workflows Based on Dataflow Process Networks (or from Ptolemy to Kepler) (or Workflow Considered Harmful.

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Presentation on theme: "NeSCR Dec-3 -2003 Bertram Ludaescher Scientific Workflows Based on Dataflow Process Networks (or from Ptolemy to Kepler) (or Workflow Considered Harmful."— Presentation transcript:

1 NeSCR Dec Bertram Ludaescher Scientific Workflows Based on Dataflow Process Networks (or from Ptolemy to Kepler) (or Workflow Considered Harmful …) Bertram Ludäscher San Diego Supercomputer Center

2 NeSCR Dec Bertram Ludaescher Overview 1.Scientific Workflow (SWF) Examples 2.SWF Requirements & Characteristics 3. Workflow standards considered harmful for SWF!? 4.Dataflow Process Networks (Ptolemy II) 5.Scientific Workflows (Kepler = Ptolemy II + X)

3 NeSCR Dec Bertram Ludaescher Acknowledgements I NSF, NIH, DOENSF, NIH, DOE GEOsciences Network (NSF)GEOsciences Network (NSF) –www.geongrid.org Biomedical Informatics Research Network (NIH)Biomedical Informatics Research Network (NIH) –www.nbirn.net Science Environment for Ecological Knowledge (NSF)Science Environment for Ecological Knowledge (NSF) –seek.ecoinformatics.org Scientific Data Management Center (DOE)Scientific Data Management Center (DOE) –sdm.lbl.gov/sdmcenter/

4 NeSCR Dec Bertram Ludaescher Acknowledgements II Ilkay Altintas SDMIlkay Altintas SDM Chad Berkley SEEKChad Berkley SEEK Shawn Bowers SEEKShawn Bowers SEEK Jeffrey Grethe BIRNJeffrey Grethe BIRN Christopher H. Brooks Ptolemy IIChristopher H. Brooks Ptolemy II Zhengang Cheng SDMZhengang Cheng SDM Efrat Jaeger GEONEfrat Jaeger GEON Matt Jones SEEKMatt Jones SEEK Edward A. Lee Ptolemy IIEdward A. Lee Ptolemy II Kai Lin GEONKai Lin GEON Bertram Ludaescher BIRN, GEON, SDM, SEEKBertram Ludaescher BIRN, GEON, SDM, SEEK Stephen Neuendorffer Ptolemy IIStephen Neuendorffer Ptolemy II Mladen Vouk SDMMladen Vouk SDM Yang Zhao Ptolemy IIYang Zhao Ptolemy II … Coming soon!?:Coming soon!?: –ROADNet, myGrid, GriPhyN,... Ptolemy II

5 NeSCR Dec Bertram Ludaescher Promoter Identification Workflow (PIW) Source: Matt Coleman (LLNL)

6 NeSCR Dec Bertram Ludaescher Promoter Identification Workflow in Ptolemy-II (SSDBM03) Execution Semantics

7 NeSCR Dec Bertram Ludaescher GARP Invasive Species Pipeline Training sample (d) GARP rule set (e) Test sample (d) Integrated layers (native range) (c) Species presence & absence points (native range) (a) EcoGrid Query EcoGrid Query Layer Integration Layer Integration Sample Data + A3 + A2 + A1 Data Calculation Map Generation Validation User Validation Map Generation Integrated layers (invasion area) (c) Species presence &absence points (invasion area) (a) Native range prediction map (f) Model quality parameter (g) Environmental layers (native range) (b) Generate Metadata Archive To Ecogrid Registered Ecogrid Database Registered Ecogrid Database Registered Ecogrid Database Registered Ecogrid Database Environmental layers (invasion area) (b) Invasion area prediction map (f) Model quality parameter (g) Selected prediction maps (h) Source: NSF SEEK (Deana Pennington et. al, UNM)

8 NeSCR Dec Bertram Ludaescher Rock & Mineral Classification Workflow

9 NeSCR Dec Bertram Ludaescher A Look Inside Classification Diagrams information and transitions between them. Extracted from the mineral composition and this levels diagram coordinates. SVG to polygons. Classifier: Locates the points region. Finer granularity Displays the point in the diagram for this level.

10 NeSCR Dec Bertram Ludaescher Source: NIH BIRN (Jeffrey Grethe, UCSD)

11 NeSCR Dec Bertram Ludaescher SWF Requirements & Characteristics Scientist friendly "problem solving environment"Scientist friendly "problem solving environment" –WF design –WF execution –WF steering and UI pause; revise; resume; rollback (cf. SCIRun) –repositories of reusable components –data and WF provenance (virtual data concept) logging, cache reuse/partial re-derive, reports, … –Conceptual modeling support complex data (semantics) support wiring support (cf. web service composition) planning support

12 NeSCR Dec Bertram Ludaescher SWF Requirements & Characteristics "Modeling" support"Modeling" support –Abstraction, hierarchical modeling –Models of Computation (MoC) –component interaction; combination of MoCs (cf. CCA) –WF multi-grain/granola: powder to bolders (and back) Boolean (N)AND, (N)OR,… vs. chaining together Grid-apps –Rich data structures and type systems End user "programming" supportEnd user "programming" support –high-level programming constructs e.g. map/3 for iteration, filter, select, branch, merge,... –data transformations –legacy tool integration (plug-ins) –data streaming How to tame (e.g., starve a dataflow; then resume)? Zauberlehrlings problem

13 NeSCR Dec Bertram Ludaescher SWF Requirements & Characteristics Grid-enabling SWFsGrid-enabling SWFs –transparent use of (remote) resources –big data –big computation requirements –early/late binding of logical to physical resources, … –planning, scheduling, … cf. Chimera, Pegasus, DAGman, Condor(-G)

14 NeSCR Dec Bertram Ludaescher Scientific Workflows: Some Findings More dataflow than (business) workflowMore dataflow than (business) workflow –but some branching looping, merging, … –not: documents/objects undergoing modifications –instead often: dataset-out = analysis(dataset-in) Need for programming extensionNeed for programming extension –Iterations over lists (foreach); filtering; functional composition; generic & higher-order operations (zip, map(f), …) Need for abstraction and nested workflowsNeed for abstraction and nested workflows Need for data transformations (compute/transform alternations)Need for data transformations (compute/transform alternations) Need for rich user interaction & workflow steering:Need for rich user interaction & workflow steering: –pause / revise / resume –select & branch; e.g., web browser capability at specific steps as part of a coordinated SWF Need for high-throughput transfers (grid-enabling, streaming)Need for high-throughput transfers (grid-enabling, streaming) Need for persistence of intermediate productsNeed for persistence of intermediate products data provenance (virtual data concept)

15 NeSCR Dec Bertram Ludaescher Scientific WF vs Business WF Scientific WorkflowsScientific Workflows –Dataflow and data transformations –Data problems: volume, complexity, heterogeneity –Grid-aspects Distributed computation Distributed data –User-interactions/WF steering –Data, tool, and analysis integration Dataflow and control-flow are married! Business WorkflowsBusiness Workflows –Process composition –Tasks, documents, etc. undergo modifications (e.g., flight reservation from reserved to ticketed), but modified WF objects still identifiable throughout –Complex control flow, task-oriented: travel reservations; credit approval Dataflow and control-flow are divorced!

16 NeSCR Dec Bertram Ludaescher A ZOO of Workflow Standards and Systems Source: W.M.P. van der Aalst et al. Source: W.M.P. van der Aalst et al.

17 NeSCR Dec Bertram Ludaescher Business Workflows Business WorkflowsBusiness Workflows –show their office automation ancestry –documents and work-tasks are passed –no data streaming, no data-intensive pipelines –lots of standards to choose from: WfMC, WSFL, BMPL, BPEL4WS,.. XPDL,… –but often no clear execution semantics for constructs as simple as this: Source: Expressiveness and Suitability of Languages for Control Flow Modelling in Workflows, PhD thesis, Bartosz Kiepuszewski, 2002

18 NeSCR Dec Bertram Ludaescher On Workflow Standards…

19 NeSCR Dec Bertram Ludaescher Workflow Standards Debunked Source: Dont go with the flow:Web services composition standards exposed,W.M.P. van der Aalst, Trends & Controversies, Jan/Feb 2003 issue of IEEE Intelligent Systems Web Services - Been there done that?

20 NeSCR Dec Bertram Ludaescher Workflow Standards Debunked Source: Dont go with the flow:Web services composition standards exposed,W.M.P. van der Aalst, Trends & Controversies, Jan/Feb 2003 issue of IEEE Intelligent Systems Web Services - Been there done that?

21 NeSCR Dec Bertram Ludaescher But never mind the standards discussion: Many Scientific Workflows are Dataflows! (Check YOUR examples …)

22 NeSCR Dec Bertram Ludaescher Commercial Workflow/Dataflow Systems

23 NeSCR Dec Bertram Ludaescher SCIRun: Component-Based Problem Solving Environments for Large-Scale Scientific Computing SCIRun: problem solving environment for interactive construction, debugging, and steering of large-scale scientific computationsSCIRun: problem solving environment for interactive construction, debugging, and steering of large-scale scientific computations Component model, based on generalized dataflow programmingComponent model, based on generalized dataflow programming Contact: Steve Parker (cs.utah.edu); SciDAC/SDM collaborationContact: Steve Parker (cs.utah.edu); SciDAC/SDM collaboration

24 NeSCR Dec Bertram Ludaescher Workflow and distributed computation grid created with Kensington Discovery Edition from InforSense.

25 NeSCR Dec Bertram Ludaescher Dataflow Process Networks: Putting Computation Models first! Synchronous Dataflow Network (SDF)Synchronous Dataflow Network (SDF) –Statically schedulable single-threaded dataflow Can execute multi-threaded, but the firing-sequence is known in advance –Maximally well-behaved, but also limited expressiveness Process Network (PN)Process Network (PN) –Multi-threaded dynamically scheduled dataflow –More expressive than SDF (dynamic token rate prevents static scheduling) –Natural streaming model Other Execution Models (Domains)Other Execution Models (Domains) –Implemented through different Directors actor typed i/o ports FIFO advanced push/pull

26 NeSCR Dec Bertram Ludaescher Dataflow Process Networks and Ptolemy-II see!see! try!try! read!read! Source: Edward Lee et al.

27 NeSCR Dec Bertram Ludaescher Why Ptolemy-II? PTII Objective:PTII Objective: –The focus is on assembly of concurrent components. The key underlying principle in the project is the use of well-defined models of computation that govern the interaction between components. A major problem area being addressed is the use of heterogeneous mixtures of models of computation. Data & Process oriented:Data & Process oriented: –Dataflow process networks Natural Data Streaming SupportNatural Data Streaming Support End user WF console (Vergil GUI)End user WF console (Vergil GUI) PRAGMATICSPRAGMATICS –mature, actively maintained, well-documented –open source system –leverage sister projects activities (e.g. SEEK, SDM, BIRN,…)

28 NeSCR Dec Bertram Ludaescher Source: Edward Lee et al.

29 NeSCR Dec Bertram Ludaescher Source: Edward Lee et al.

30 NeSCR Dec Bertram Ludaescher Marrying & Divorcing Control- & Dataflow Source: Edward Lee et al.

31 NeSCR Dec Bertram Ludaescher Another Goodie: Ptolemy-II Type System

32 NeSCR Dec Bertram Ludaescher Support for Multiple Workflow Granularities Bolders Abstraction: Sand to Rocks Sand Powder Plumbing

33 NeSCR Dec Bertram Ludaescher Scientific Workflows = Dataflow Process Networks + X X = …X = … –Database plug-ins –Legacy application plug-ins (via command line, as web services, …) –Grid extensions: Actors as web/grid services 3 rd party data transfer, high-throughput data streaming Dealing with thousands of files (cf. astrophysics, astronomy, HEP, … examples) Data and service repositories, discovery Extended type system (structural & semantic extensions) –Programming extensions (declarative/FP) and –Rich user interactions/workflow steering –Rich data transformations (compute/transform alternations) –Data provenance (semi-)automatic meta-data creation Kepler = Ptolemy-II + X

34 NeSCR Dec Bertram Ludaescher Status update / specific tasks for Kepler $DONE, %ONGOING, *NEW User interaction, workflow steeringUser interaction, workflow steering –$ Pause/revise/resume –$ BrowserUI actor (browser as a 0-learning display and selection tool) Distributed executionDistributed execution –$ Dynamically port-specializing WSDL actor –* Dynamically specializing Grid service actor Port & actor type extensions (SEEK leverage)Port & actor type extensions (SEEK leverage) –* Structural types (XML Schema) –* Semantic types (OWL) incl. unit types w/ automatic conversion Programming extensionsProgramming extensions –% Data transformation actors (XSLT, XQuery, Python, Perl,…) –* map, zip, zipWith, …, loop, switch patterns Specialized Data SourcesSpecialized Data Sources –$ EML (SEEK), –% MS Access (GEON), *JDBC, –*XML, *NetCDF, …

35 NeSCR Dec Bertram Ludaescher Some specific tasks for Kepler (all NEW) Design & develop transparent, Grid-enabled PNs:Design & develop transparent, Grid-enabled PNs: –Communication protocol details –Grid-actor extensions and/or –Grid-Process Network director (G-PN) –Host/Source-location becomes actor parameter add active-inline parameter display for grid-actors channels source-actors Activity MonitoringActivity Monitoring –Add activity status display (green, yellow, red) to replace PtII animation (needed for concurrently executing PN!) Registration & Deployment mechanismsRegistration & Deployment mechanisms –Actor/Data/Workflow repository (=composite actors) –Shows up as (configable) actor library –OGSA Service Registry approach? ( SEEK leverage; UDDI complex & limited says MattJ ) Extensions to deal with failures (fault tolerance)Extensions to deal with failures (fault tolerance)

36 NeSCR Dec Bertram Ludaescher Example: Database actors for Ptolemy II (Kepler-GEON; Efrat Jaeger)

37 Database Actors Database Connection actor:Database Connection actor: Database Query actor:Database Query actor:

38 Database Actors Example

39 NeSCR Dec Bertram Ludaescher Example: Web service-enabling Ptolemy II (Kepler-SDM; Ilkay Altintas)

40 NeSCR Dec Bertram Ludaescher A Generic Web Service Actor Configure – select WSDL url from repository Configure - select service operation

41 NeSCR Dec Bertram Ludaescher Set Parameters and Commit Specialized Actor Set parameters and commit

42 NeSCR Dec Bertram Ludaescher Web Service Actor after Instantiation

43 NeSCR Dec Bertram Ludaescher Composing Third-Party Web Services Output of previous web service User interaction & Transformations Input of next web service

44 NeSCR Dec Bertram Ludaescher Results of the Execution User I/O via standard brower! Run Window / WF Deployment

45 NeSCR Dec Bertram Ludaescher Composing Legacy Applications (here: Phylogeny): Shell / Command-Line Actors

46 NeSCR Dec Bertram Ludaescher Example: Grid-enabling Ptolemy II ( Kepler-SEEK, Chad Berkley Kepler-SDM, Ilkay Altintas, … myGrid?, … …GriPhyN?, … … OGS{I|A}-[DAI]...)

47 NeSCR Dec Bertram Ludaescher Transparently Grid-Enabling PTII: Handles AB GAGB 1.A GA: get_handle 2.GA A: return &X 3.A B: send &X 4.B GB: request &X 5.GB GA: request &X 6.GA GB: send *X 7.GB B: send done(&X) Example: &X = GA.17 *X = PTII space Grid space Logical token transfer (3) requires get_handle(1,2); then exec_handle(4,5,6,7) for completion.

48 NeSCR Dec Bertram Ludaescher Transparently Grid-Enabling PTII Different phasesDifferent phases –Register designed WF (could include external validation service) –Find suitable grid service hosts for actors –Pre-stage execution –Execute (w/ provenance) Interactively steer (pause; revise; resume) Batch process; re-run parts later –Register/store data products and execution logs Kepler implementation choices:Kepler implementation choices: –Grid-actors (no change of Director necessary!?) and/or –Grid-(PN)-director (also need to change actors!?) –Add grid service host id as actor parameter: –Similar for data:

49 NeSCR Dec Bertram Ludaescher C-z ; bf & – Detach your WF execution! Currently in PTIICurrently in PTII –tight coupling of WF execution and PTII Java client (also Vergil GUI) To-do for Kepler:To-do for Kepler: –detaching WF console (Vergil) from a Grid-aware execution engine Grid-PN Director! Transport protocol parameter Data location parameter Host location parameter

50 NeSCR Dec Bertram Ludaescher Semantic Type-enabling Ptolemy II (OWL – here we go… ;-) (Kepler-SEEK; Shawn Bowers)

51 NeSCR Dec Bertram Ludaescher Semantic Type Extensions Take concepts and relationships from an ontology to semantically type the data-in/out portsTake concepts and relationships from an ontology to semantically type the data-in/out ports Application: e.g., design support:Application: e.g., design support: –smart/semi-automatic wiring, generation of massaging actors m 1 (normalize) p3p3 p4p4 Takes Abundance Count Measurements for Life Stages Returns Mortality Rate Derived Measurements for Life Stages

52 NeSCR Dec Bertram Ludaescher

53

54 Semantic Types The semantic type signatureThe semantic type signature –Type expressions over the (OWL) ontology m 1 (normalize) p3p3 p4p4 SemType m1 :: Observation & itemMeasured.AbundanceCount & hasContext.appliesTo.LifeStageProperty -> DerivedObservation & itemMeasured.MortalityRate & hasContext.appliesTo.LifeStageProperty

55 NeSCR Dec Bertram Ludaescher Extended Type System (here: OWL Semantic Types) SemType m1 :: Observation & itemMeasured.AbundanceCount & hasContext.appliesTo.LifeStageProperty DerivedObservation & itemMeasured.MortalityRate & hasContext.appliesTo.LifeStageProperty Substructure association: XML raw-data =(X)Query=> object model =link => OWL ontology

56 NeSCR Dec Bertram Ludaescher Programming Extensions (some lessons from SciDAC/SSDBM demo)

57 NeSCR Dec Bertram Ludaescher Promoter Identification Workflow in Ptolemy-II (SSDBM03) hand-crafted control solution; also: forces sequential execution! designed to fit hand-crafted Web-service actor Complex backward control-flow No data transformations available

58 NeSCR Dec Bertram Ludaescher Promoter Identification Workflow in FP genBankG :: GeneId -> GeneSeq genBankP :: PromoterId -> PromoterSeq blast :: GeneSeq -> [PromoterId] promoterRegion :: PromoterSeq -> PromoterRegion transfac :: PromoterRegion -> [TFBS] gpr2str :: (PromoterId, PromoterRegion) -> String d0 = Gid "7" -- start with some gene-id d1 = genBankG d0 -- get its gene sequence from GenBank d2 = blast d1 -- BLAST to get a list of potential promoters d3 = map genBankP d2 -- get list of promoter sequences d4 = map promoterRegion d3 -- compute list of promoter regions and... d5 = map transfac d get transcription factor binding sites d6 = zip d2 d4 -- create list of pairs promoter-id/region d7 = map gpr2str d6 -- pretty print into a list of strings d8 = concat d7 -- concat into a single "file" d9 = putStr d8 -- output that file

59 NeSCR Dec Bertram Ludaescher Cleaned up Process Network PIW Back to purely functional dataflow process networkBack to purely functional dataflow process network (= also a data streaming model!) Re-introducing map (f) to Ptolemy-II (was there in PT Classic)Re-introducing map (f) to Ptolemy-II (was there in PT Classic) no control-flow spaghetti data-intensive apps free concurrent execution free type checking automatic support to go from piw(GeneId) to PIW := map (piw) over [GeneId] map (f)-style iterators Powerful type checking Generic, declarative programming constructs Generic data transformation actors Forward-only, abstractable sub- workflow piw(GeneId)

60 NeSCR Dec Bertram Ludaescher Optimization by Declarative Rewriting I PIW as a declarative, referentially transparent functional processPIW as a declarative, referentially transparent functional process optimization via functional rewriting possible e.g. map(f o g) = map(f) o map(g) Details:Details: –Technical report &PIW specification in Haskell map(f o g) instead of map(f) o map(g) Combination of map and zip

61 NeSCR Dec Bertram Ludaescher Optimizing II: Streams & Pipelines Clean functional semantics facilitates algebraic workflow (program) transformations (Bird-Meertens); e.g. mapS f mapS g mapS (f g)Clean functional semantics facilitates algebraic workflow (program) transformations (Bird-Meertens); e.g. mapS f mapS g mapS (f g) Source: Real-Time Signal Processing: Dataflow, Visual, and Functional Programming, Hideki John Reekie, University of Technology, Sydney

62 NeSCR Dec Bertram Ludaescher Summary Many (most of ours anyways) scientific workflows are dataflowsMany (most of ours anyways) scientific workflows are dataflows –lots of workflow standards (messy and not focused on SWF problems) –should we start a new wave of dataflow standards?? Importance of clear semantics forImportance of clear semantics for –different MoCs (models of computation: PN, SDF, DE, CT, …) –component composition across MoCs –component interaction – Ptolemy II directors Kepler:Kepler: –Based on extensible Ptolemy II system –Cross-project activity (SEEK, SDM, Ptolemy II, GEON, BIRN, and counting) –Plug-in / interface with your SWF planner, execution engine, grid-WF tool!

63 NeSCR Dec Bertram Ludaescher Your Projects & Icons Your Projects & Icons

64 NeSCR Dec Bertram Ludaescher A Note on the Style of these Slides Due to lack of time, most of the following slides are by reference only ;-) – …Each speaker was given four minutes to present his paper, as there were so many scheduled from 64 different countries. To help expedite the proceedings, all reports had to be distributed and studied beforehand, while the lecturer would speak only in numerals, calling attention in this fashion to the salient paragraphs of his work.... Stan Hazelton of the U.S. delegation immediately threw the hall into a flurry by emphatically repeating: 4, 6, 11, and therefore 22; 5, 9, hence 22; 3, 7, 2, 11, from which it followed that 22 and only 22!! Someone jumped up, saying yes but 5, and what about 6, 18, or 4 for that matter; Hazelton countered this objection with the crushing retort that, either way, 22. I turned to the number key in his paper and discovered that 22 meant the end of the world… [The Futurological Congress, Stanislaw Lem, translated from the Polish by Michael Kandel, Futura 1977]

65 NeSCR Dec Bertram Ludaescher F I N: Words to/from the Wise FYI: Flow-based programming has been re-discovered/re-invented several times by different communities. Here is an IBM practitioners view: – Flow-based Programming, … In "Flow-Based Programming" (FBP), applications are defined as networks of "black box" processes, which exchange data across predefined connections. These black box processes can be reconnected endlessly to form different applications without having to be changed internally. It is thus naturally component-oriented. To describe this capability, the distinguished IBM engineer, Nate Edwards, coined the term "configurable modularity", which he calls the basis of all true engineered systems. When using FBP, the application developer works with flows of data, being processed asynchronously, rather than the conventional single hierarchy of sequential, procedural code. It is thus a good fit with multiprocessor computers, and also with modern embedded software. In many ways, an FBP application resembles more closely a real-life factory, where items travel from station to station, undergoing various transformations. Think of a soft drink bottling factory, where bottles are filled at one station, capped at the next and labelled at yet another one. FBP is therefore highly visual: it is quite hard to work with an FBP application without having the picture laid out on one's desk, or up on a screen! For an example, see Sample DrawFlow Diagram.Sample DrawFlow Diagram Strangely though, in spite of being at the leading edge of application development, it is also simple enough that trainee programmers can pick it up, and it is a much better match with the primitives of data processing than the conventional primitives of procedural languages. The key, of course (and perhaps the reason why it hasn't caught on more widely), is that it involves a significant paradigm shift that changes the way you look at programming, and once you have made this transition, you find you can never go back! FBP seems to dovetail neatly with a concept that I call "smart data". There is a section on this in stuff about the author. A new web page on this topic has just been uploaded - see "Smart Data" and Business Data Types - and we will be publishing more as it develops.stuff about the author"Smart Data" and Business Data Types …


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