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Kepler Project Overview, Status, Future Directions. Bertram Ludäscher University of California, Davis. Overview. History Origins, Diversity, Challenges Kepler, Kepler/CORE: Issues, Status Next steps Research: Scientific Workflows … Business (workflows) as usual? Or… ?.

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Kepler project overview status future directions

Kepler Project Overview, Status, Future Directions

Bertram Ludäscher

University of California, Davis



  • History

    • Origins, Diversity, Challenges

  • Kepler, Kepler/CORE:

    • Issues, Status

    • Next steps

  • Research: Scientific Workflows …

    • Business (workflows) as usual?

    • Or… ?

Kepler some history

Kepler: Some History

  • The Origins:

    • AD 2002: NSF/SEEK, DOE/SDM

      • Similar requirements for “scientific workflows”

      • Can we avoid reinventing the wheel (twice…) !?

      • Grass-roots effort, open source collaboration

  • The Head-start:

    • Adopting, extending Ptolemy II from Berkeley

    • Common software platform facilitates grass-root collaboration

    • More than software: Research Results

      • Heterogeneous Modeling & Design, dataflow-, actor-oriented MoCs

Scientific workflows cyberinfrastructure upperware






Scientific Workflows: Cyberinfrastructure “Upperware”

NSF/SEEK ITR, 5 Year collaboration: SDSC, UCSB, UCD, UNM, UK, …

Scientific workflow

Scientific Workflow

Capture how a scientist works with data and analytical tools

  • data access, transformation, analysis, visualization

  • possible worldview: dataflow-oriented (cf. signal-processing)

    Scientific workflow (wf) benefits (compare w/ script-based approaches) :

  • wf automation

  • wf & component reuse

  • wf design, documentation

  • wf archival, sharing

  • built-in concurrency

    (task-, pipeline-parallelism)

  • built-in provenance support

  • distributed & parallel exec:

    Grid & cluster support

Simple kepler analysis workflow using r

Data source from EcoGrid

(metadata-driven ingestion)

R processing script

res <- lm(BARO ~ T_AIR)


plot(T_AIR, BARO)


Simple Kepler analysis workflow using R …

Dan Higgins, NCEAS

Vs plumbing workflow

to monitor, control remote supercomputer simulations …

50+ composite actors (subworkflows)

4 levels of hierarchy

1000+ atomic (Java) actors

43 actors, 3 levels

196 actors, 4 levels

30 actors

206 actors, 4 levels

33 actors

137 actors

123 actors


66 actors

12 actors

243 actors, 4 levels

… vs. “Plumbing” workflow

Norbert Podhorszki

Then: UC Davis, now: ORNL …

Kepler open source open community

Kepler: Open Source + Open Community

  • Huge diversity of domains => needs

    • Astrophysics, nuclear fusion research, geoinformatics, ecology, systematics, bioinformatics, genomics, environmental monitoring, simulation, …

    • Not just bioinformatics and cheminformatics …

  • A broad range of technical problems

    • Workflowdesign with a graphical UI

    • Sharing actors, workflows across communities

    • Distributed workflow execution

    • Data movement on the network

    • Integrate local apps, web services, native actors

    • Support a variety of computational models

    • Not just web service orchestration or Grid deployment

Kepler open source open community1

Kepler: Open Source + Open Community

3. Many kinds of users with different backgrounds and responsibilities:

  • Scientistsautomating and sharing their analyses of their own data or performing meta-analyses on others’ data

  • Software engineersdeveloping their own systems around Kepler

  • Computer scientists doing basic research in scientific workflows, data and provenance management, distributed and collaborative computing.

  • Not just biologists and chemists …

    4. Kepler used in many different deployment contexts

  • Standalone application on a scientist’s desktop computer or laptop.

  • Backend for web-based scientific applications.

  • Embedded workflow engine in larger systems.

  • One size (of deployment) does not fit all!

    5. Kepler open to contribution and extension by anyone:

  • Anyone can contribute to Kepler!

  • Anyone canuse Kepler in their own applications

  • Developing with Kepler doesn’t require collaboration with the “owners” …

Kepler project overview status future directions













Library Science






Molecular Biology


Particle Physics

Kepler core mission

Kepler-CORE Mission

In collaboration with current and future contributors to Kepler, the Kepler/CORE team will …

  • Develop and maintain the essential, interdisciplinary software components of Kepler

  • Coordinate the contributions of the greater Kepler collaboration to the core (“kernel”) of the system

  • Increase the role of the current and future user community in specifying requirements and priorities

The kepler core project and team

The Kepler-CORE Project and Team

  • Kepler-CORE(sensu stricto)

    • 3-year, $1.7M NSF-OCI funded project

  • Kepler-CORE team @ UCD,UCSB, UCSD:

    • UC Davis

      • Bertram Ludäscher (PI@UCD), Shawn Bowers (co-PI), Tim McPhillips (co-PI & software architect), David Welker (software engineer), Sean Riddle (software engineer)

    • UC Santa Barbara

      • Matthew Jones (PI@UCSB), Mark Schildhauer (co-PI), Aaron Schultz, Chad Berkley (software engineer)

    • UC San Diego

      • Ilkay Altintas (PI@SDSC), Jianwu Wang (postdoc)

  • Kepler/CORE(sensu lato)

    • Goal: sustain long-term, beyond initial funding period

    • KEPLER = Kepler/Core + Kepler/X + Kepler/Y + …

      • Core, X, Y, … = open community of stakeholders, contributors, users, etc.

Kepler core vision

Kepler-CORE Duo

Kepler-CORE Vision

In the future we foresee Kepler…

  • Satisfying the scientific workflow automation needs of

    • Collaborative government-funded projects

    • Academic research groups

    • Individual researchers in diverse scientific disciplines

  • Enhancing the productivity of researchers by

    • Facilitating discovery and collaboration within and across disciplines

    • Being the best way for scientists to leverage developments and expertise in other domains

  • Leading to further breakthroughs and innovations in the fields of

    • Scientific data management

    • Data provenance

    • Collaborative scientific computing

  • Shepherded by a self-sustaining effort that thrives well beyond the lifetimes of the grants that have contributed to Kepler’s development.

These differences mean that the kepler collaboration will be unique too

These differences mean that the Kepler collaboration will be unique, too

  • Kepler cannot solve everyone’s problems right out of the box

    • Kepler must be adaptable to different domain sciences

    • Adaptation requires more than developing new actors

    • Kepler is as much a development platform as an “end-user” tool

    • No one group can take responsibility for supporting all the ways Kepler will be used …

  • Kepler is open-source but more complex than other open source projects

    • Diversity of domains, users, and deployment contexts mean there can be conflicts between the needs or priorities of contributors

    • Need a way of developing and adding extensions without breaking other’s systems

    • Software engineers developing code for Kepler often are not expert scientists, and cannot be the final authority on what the system should do (unlike projects like Apache, Linux, etc where the engineers are the expert users themselves and can add what they need)

    • PIs and project managers on projects extending Kepler must take responsibility for knowing what needs to be done.

    • It is essential that for each project employing Kepler, representatives authoritative on the scientific and technical needs of their projects participate in driving the future development of Kepler!

Stakeholders essential to success of kepler

Stakeholders: Essential to Success of Kepler

Kepler stakeholders …

  • Are projects and individuals whose work depend critically on the success of Kepler.

  • Are funded by a variety of sources and work in diverse fields of scientific research.

  • Are more likely to greatly extend Kepler and use Kepler within their own systems than simply develop packages of actors and workflows for use with a standard distribution of Kepler.

  • Need to deliver the software systems they develop to their own community of users.

  • Must deliver their software systems according to their own (e.g. release) schedules as determined by their research and funding programs.

  • Have different requirements that will conflict in the absence of mechanisms for enabling independent extension and deployment of Kepler-based systems.

  • Require recognition for the contributions they make to Kepler as well as for their own systems based on Kepler.

  • Know better than us what they need from Kepler.

Kepler core management

Kepler(-CORE) Management

  • Leadership Team

    • 3 year terms (current members from UC + [S] + {B, D} )

    • Focus on

      • long term viability of Kepler

      • strategic decisions on behalf of user community, Kepler project

  • Interest Groups

    • Communicate, collaborate on specialized capabilities

  • Development Teams

    • Design, develop, test specific software deliverables

  • Infrastructure Teams

    • Identify, discuss, design, implement Kepler Framework

New kepler build system

New Kepler Build System

  • Modules and suites

    • Develop against trunk or specific version, release

    • Tag, branch Kepler extensions independently of the kernel

    • easier to share develop, share extensions

    • svn repository (

  • Module Manager

    • New component to simplify

      working with modules

David Welker et al.

Kepler release roadmap

Kepler Release Roadmap


  • Kepler releases

    • based on a “standard set” of modules

  • Individual module releases

    • Provenance

    • Workflow reporting

    • COMAD

    • Distributed: Master/Slave

  • Kepler 2.0

    • Add modules, extensions to installed Kepler dynamically

    • Targeted for Summer 2009

New plone based web site forums

New Plone-based Web site, Forums

Kepler reap workflow run manager

Kepler/REAP: Workflow Run Manager

Derik Barseghian et al.

  • Use case “Publication-Ready Archive”

  • Archive workflow with inputs, outputs

  • Tagging

  • Also: Outline view

    • to manage browsing of large/deeply nested workflows

Workflow reports provenance interest group

Workflow Reports (Provenance Interest Group)

Derik Barseghian et al.

Kepler and scientific workflow research

Kepler and Scientific Workflow Research

  • Scientific Workflows:

    • Business (workflows) as usual?

      • Data-oriented, data-centric …

        • … as opposed to control-, task-centric

    • Signal processing?

    • Or else …?

  • Modeling scientific processes, analysis methods

    • Understanding is in the Mind of the Beholder!

  • Example areas:

    • Workflow Modeling & Design

    • Provenance

    • Optimization

Modeling example chip chip workflow

Modeling Example (ChIP-chip workflow)

Tim McPhillips et al.

Modeling design the limits of my language mean the limits of my world

Vanilla Process Network

Functional Programming Dataflow Network

XML Transformation Network

Collection-oriented Modeling & Design framework (COMAD)

“Look Ma: No Shims!”

Modeling & Design: The limits of my language mean the limits of my world …

Two different workflow designs

Two different workflow designs

Data provenance



















































Data Provenance

  • Keep track of data dependencies, processing history

     support interpretation, validation, reproducibility






Kepler ppod provenance browser

Kepler/pPOD: Provenance Browser

Shawn Bowers et al.

For conventional data provenance and

fine-grained dependencies (COMAD style)

Navigate forward and backward in time (VCR style) in different views (collections, processes, combined)

Collection history

Collection History

  • Collection and invocation view

  • Incrementally step through execution history

From mocs to models of provenance mops

From MoCs to Models of Provenance (MoPs)

Fine grained data moc aware mop

Fine-grained, Data & MoC-aware MoP

Manish Anand, Shawn Bowers, et al.

Optimization multi level workflows

Optimization: Multi-level Workflows



Daniel Zinn et al.

Modeling optimization virtual assembly lines comad

Modeling + Optimization: Virtual Assembly Lines (COMAD)

Layers in comad xml pipelines

Layers in COMAD / ∆-XML Pipelines

WF Graph

  • Access data in XML stream

  • Call Scientific Functions (Services)

  • Put results back into stream




Scientific Functions


Out: (t:Tree, s:score)+

In: DNASeq+

Thres: Float

Method: String

Daniel Zinn (UC Davis)

Conventional vs assembly line comad thinking

Conventional vs Assembly Line / COMAD Thinking

Daniel Zinn (UC Davis)

More secret sauce user vs optimized dataflow

More secret sauce: User vs. Optimized Dataflow

Conceptual pipeline w scopes types

Conceptual Pipeline w/ Scopes & Types

Daniel Zinn (UC Davis)

X csr xml scissor cut ship reassemble

X-CSR (“XML Scissor”): Cut-Ship-Reassemble

Daniel Zinn (UC Davis)



Department of

Computer Science


  • Kepler contributors

    • Many individuals:

    • Projects: Ptolemy, SEEK, SDM, CPES, GEON, REAP, CIPRes, ChIP2, pPOD, COMET, BAP, LTER, RAPR, ITER, …

  • Funding agencies: NSF, DOE, …

  • DAKS @ UC Davis Members

    • Research Staff

      • Drs. Shawn Bowers, Timothy McPhillips, Lei Dou, Ustun Yildiz

    • Developers:

      • Sean Riddle, David Welker, Gongjing Cao

    • Students:

      • Manish Anand, Dave Thau, Daniel Zinn, Sven Koehler, Saumen Dey, Supriya Gulati, Faraaz Sareshwala, Xuan Li

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