1. Workflow Topics for the Next- Generation SDM-Center Ilkay Altintas altintas@SDSC.edu Bertram Ludäscher ludaesch@UCDAVIS.edu San Diego Supercomputer Center UC DAVIS Department of Computer Science SciDAC SDM AHM Oct 5-6, 2005, NCSU Raleigh, NC SciDAC SDM AHM Oct 5-6, 2005, NCSU Raleigh, NC Sir Walter Raleigh

2. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Overview Kepler/SPA: –What we have (The GOOD) –What we don’t (yet) have (The BAD) –What we really need?? (The UGLY)  Things we might do; prioritization

3. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Macro Definitions … #define KEPLER KEPLER/SPA #define KEPLER KEPLER*SPA By the end: #define SPA KEPLER HPC

4. SDM-AHM-10-05 NCSUNext SDM-C: Workflows What we have – The GOOD Big Heritage from Ptolemy II –Vergil GUI for design and (some) execution monitoring –Actor-Oriented Modeling & Design Director / Actor Separation Models of Computation: PN, SDF, DE,.. Nested Workflows & Hierarchical Modeling Research Results on Modeling Complex Systems –modal models, mobile models, reconfig’able models, model lifecycle management, higher-order actors, …  head-start for CCA Extensions, e.g. SciRUN-2 Extensions (Steve P. et al.) Self-Managing, Dynamically-Adaptive, Autonomous Components (Manish et al.)

5. SDM-AHM-10-05 NCSUNext SDM-C: Workflows What we have – The GOOD Kepler Extensions (to Ptolemy II) –Mostly: loosely coupled, e.g. WS (web service) workflows –Many generic actors ssh, scp, cmd-line,SRB, Globus, … new R expression actor –Many custom actors e.g. in PIW, TSI-1, TSI-2, GEON, SEEK, Resurgence, … –Several ad-hoc extensions & (initial) research, e.g. External job scheduling (e.g. NIMROD, …) Director extensions (fault tolerance via WS “retry”) WF-Templates (structured combination of dataflow & control-flow: fault-tolerance, reusability) Higher-order functions (map/3, iterate-over-array, … : simpler control-flow, optimization potential, …)

6. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Some KEPLER Actors (out of 160+ … and counting…)

7. SDM-AHM-10-05 NCSUNext SDM-C: Workflows What we have – The GOOD Kepler Extensions (Cont’d) –Some generic extensions Metadata-based (EML/ADN) Dataset Search Concept-based Actor Search (OWL) Documentation Framework Authentication & Authorization Framework (GAMA from GEON) Improved component/WF archival & plug-in (KAR,…) Provenance Recorder (“Listener”) PS … a growing open-source developers community … … and some scientific users … (TSI-1/2, PIW, GEON, SEEK, … )

8. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Concept-based Actor Search –Implemented as proof-of- concept Additional operations slated for next Kepler Release (data search, port-based actor search, etc.) Biggest Challenges –Building/searching a repository … –Making changes to MoML (see KAR) –GUI changes –Ontology management Concept-based Actor Search Workflow Components (MoML/KAR) Ontologies (OWL) Default + Other Semantic Annotations urn ids instance expressions

9. SDM-AHM-10-05 NCSUNext SDM-C: Workflows

10. SDM-AHM-10-05 NCSUNext SDM-C: Workflows The GOOD: Kepler Archives Purpose: Encapsulate WF data and actors in an archive file –… inlined or by reference –… version control  More robust workflow exchange  Easy management of semantic annotations  Plug-in architecture (Drop in and use)  Easy documentation updates A jar-like archive file (.kar) including a manifest All entities have unique ids (LSID) Custom object manager and class loader UI and API to create, define, search and load.kar files

11. SDM-AHM-10-05 NCSUNext SDM-C: Workflows KAR File Example

12. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Kepler Object Manager Designed to access local and distributed objects Objects: data, metadata, annotations, actor classes, supporting libraries, native libraries, etc. archived in kar files Advantages: –Reduce the size of Kepler distribution Only ship the core set of generic actors and domains –Easy exchange of full or partial workflows for collaborations –Publish full workflows with their bound data Becomes a provenance system for derived data objects => Separate SPA workflow repository and distribution

13. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Provenance Framework Provenance –Track origin and derivation information about scientific workflows, their runs and derived information (datasets, metadata…) Need for Provenance –Association of process and results –reproduce results –“explain & debug” results (via lineage tracing, parameter settings, …) –optimize: “Smart Re-Runs” Types of Provenance Information: –Data provenance Intermediate and end results including files and db references –Process (=workflow instance) provenance Keep the wf definition with data and parameters used in the run –Error and execution logs –Workflow design provenance (quite different) WF design is a (little supported) process (art, magic, …) for free via cvs: edit history need more “structure” (e.g. templates) for individual & collaborative workflow design

14. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Kepler Provenance Recording Utility Parametric and customizable –Different report formats –Variable levels of detail Verbose-all, verbose-some, medium, on error –Multiple cache destinations Saves information on –User name, Date, Run, etc… Joint work with Oscar Barney

15. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Provenance: Next Steps.kar file generation, registration and search for provenance information Possible data/metadata formats Automatic report generation from accumulated data A relational schema for the provenance info in addition to the existing XML Smart re-runs

16. SDM-AHM-10-05 NCSUNext SDM-C: Workflows The Future From GOOD via BAD to UGLY The good news (about ‘bad’ and ‘ugly’) –Lots of interesting challenges! –… so ‘ugly’ is actually good!

17. SDM-AHM-10-05 NCSUNext SDM-C: Workflows What we don’t (yet) have … THE BAD Much is still to do (or still ongoing) –Detached execution many options; depend on requirements –Kepler WF repository w/ dynamic actor plug-in –Smart Reruns avoid doing (old) work twice –Smarter Reruns (too smart?) reuse previous results for speed-up of (new) work –NIMROD Director, CONDOR Director … –Task manager / monitor –Support for WF design & reuse Semantic extensions “Design Patterns”, Templates

18. SDM-AHM-10-05 NCSUNext SDM-C: Workflows What we don’t have … THE BAD cont’d Vertical SDM Integration –Workflow layer could be used to embed other SDM components and glue them together –Scope & Architecture unclear Data Mining tools  new WF actors Parallel-R  new WF actors !? SEA, Bitmap tools  new !? MPI-IO  alternative to current Kepler data access!? … –Not only a technical problem e.g. need for driving use-cases that require combination of several SDM layers together

19. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Challenges Easier said … –“We’re not going to reinvent the wheel …” –“We just use XYZ …” XYZ in {CCA, HDF5, PnetCDF, Ccaffeine, Condor, MPI-IO, parallel-R, …} … than done … –Incompatible, isolated solutions and frameworks –Can’t use workflow/actor/director A with B Coming up with a coherent, overall architecture is hard!

20. SDM-AHM-10-05 NCSUNext SDM-C: Workflows HTC Example (using: NIMROD) need to make Kepler NIMROD/Condor/… “aware” similar need for HPC support

21. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Another Distribution Approach Client Servers Computer Network Service Locator (Peer Discovery) Simulation is orchestrated in a centralized manner Source: Daniel Lázaro Cuadrado, Aalborg University

22. SDM-AHM-10-05 NCSUNext SDM-C: Workflows What we don’t have … THE UGLY Workflow Design & (Re-)Usability –Difficult Marriage of Dataflow and Control-flow e.g. PIW, TSI-1/2, GEON-A-type-WF, … –WF development, deployment, maintenance, use from (Mess…) to Art to Commodity (  next presentation) –support for WF whole life-cycle Fault Tolerance –current embedding of control-flow into dataflow yields to non- maintainable workflows! Close Coupling of Components for HPC –CCA-style –MPI-style –Memory-to-Memory (on single nodes) –large, efficient data transfer –…

23. SDM-AHM-10-05 NCSUNext SDM-C: Workflows WF-Design: Adapters for Semantic & Structural Incompatibility Adapters may: –be abstract (no impl.) –be concrete –bridge a semantic gap –fix a structural mismatch –be generated automatically (e.g., Taverna’s “list mismatch”) –be reused components (based on signatures) C1C1 C1C1 D1D1 C1C1 C2C2 CDC CD D DD C2C2 C2C2 D2D2 f2f2 f1f1 [S] S T f1f1 [T] f2f2 map f2f2 f1f1 [[S]] S T f1f1 [[T]] f2f2 map

24. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Additional Design Primitives for Semantic Types Extended TransformationsStarting WorkflowResulting Workflow t 9 : Actor Semantic Type Refinement (T T) T t 12 : I/O Constraint Strengthening (    ) t 10 : Port Semantic Type Refinement (C C, D D) C t 14 : Adapter Insertion T t 11 : Annotation Constraint Refinement (    ) s C 11  t 15 : Actor Replacement f f t 16 : Workflow Combination (Map) t 13 : Data Connection Refinement … f1f1 f2f2 f1f1 … f2f2  Resulting Workflow D C DC D t D 22 11 t D 22 s C 11 t D 22 s C

25. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Workflow Design Primitives End-to-End Workflow Design and Implementation –Viewed as a series of primitive “transformations” –Each takes a WF and produces a new WF –Can be combined to form design “strategies” W0W0 t W1W1 W2W2 WmWm WnWn … t t Workflow Design Workflow Implementation Top-Down Bottom-Up Input Driven Output Driven Structure Driven Semantic Driven Task Driven Data Driven

26. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Fault Tolerance & Maintenance Challenges

27. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Workflow Templates and Patterns New Ingredients Proposed Layered Architecture work w/ Anne Ngu, Shawn Bowers, Terence Critchlow

28. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Use Ideas from Fault Tolerant Shell Source: Douglas Thain, Miron Livny The Ethernet Approach to Grid Computing Good ideas in ftsh; some might be (semi-)low hanging fruits for Kepler …

29. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Kepler Coupling Components & Codes Types of Coupling … –Loosely coupled (“1 st Phase”) Web Services (SPA, GEON, SEEK, …), ssh actors,.. + reusability (behavorial polymorphism) + scalability (# components) – efficiency –Tight(er) coupling (“2 nd Phase”) Via CCA (SciRUN-2, Ccaffeine, …) (Cipres uses CORBA) HPC needs: code-coupling as efficient & flexible as possible (e.g. Scott’s challenges…) –memory-to-memory (single node or shared memory), –MPI (multiple-nodes) –optimizations for transfer of data & control (streaming, socket-based connections)

30. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Accord-CCA: Ccaffeine w/ Self-Managed Behavior Source: Hua Liu and Manish Parashar cf. w/ mobile models, reconfiguration in Ptolemy II … begging for a Kepler design and implementation …

31. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Different “Directors” for Different Concerns Example: –Ptolemy Directors – “factoring out” the concern of workflow “orchestration” (MoC) –common aspects of overall execution not left to the actors Similarly: –“Black Box” (“flight recorder”) a kind of “recording central” to avoid wiring 100’s of components to recording-actor(s) –“Red Box” (error handling, fault tolerance) use ftsh ideas; tempaltes –“Yellow Box” (type checking) for workflow design –“Blue Box” (shipping-and-handling) central handling of data transport (by value, by reference, by scp, SRB, GridFTP, …) –“CCA++ Boxes” Change behavior (e.g. algorithm) of a component Change behavior (i.e., wiring) of a workflow in-flight SDF/PN/DE/… Provenance Recorder SHA @ Static Analysis On Error Component Mgr Composition Mgr

32. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Summary The GOOD: –lots to build upon The BAD: –no common / integrated architecture  use Kepler/SPA as a glue  this might be harder than it sounds  needs a mix of end-to-end application-drive and serious design effort for the integration architecture The UGLY: –HPC challenges: close coupling, fault tolerance, … –The good news: there’s work to be done!

33. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Use of Semantics in SWF… “Smart” Search –Concept-based, e.g., “find all datasets containing biomass measurements” Improved Linking, Merging, Integration –Establishing links between data through semantic annotations & ontologies –Combining heterogeneous sources based on annotations –Concatenate, Union (merge), Join, etc. Transforming –Construct mappings from schema S1 to S2 based on annotations Semantic Propagation –“Pushing” semantic annotations through transformations/queries

34. SDM-AHM-10-05 NCSUNext SDM-C: Workflows Helping with “shims” / adapters Services can be semantically compatible, but structurally incompatible Source Actor Source Actor Target Actor Target Actor PsPs PtPt Semantic Type P s Semantic Type P t Structural Type P t Structural Type P s Desired Connection Incompatible Compatible (⋠)(⋠) (⊑)(⊑) (Ps)(Ps) (Ps)(Ps)  (≺)(≺) Ontologies (OWL) Source: [Bowers-Ludaescher, DILS’04]