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Cyberenvironments: Adaptive Middleware for Scientific Cyberinfrastructure ARM’07 Jim Myers, Bob McGrath jimmyers@ncsa.uiuc.edu National Center for Supercomputing Applications (NCSA), University of Illinois at Urbana-Champaign Outline What’s Changing in Science?

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Cyberenvironments: Adaptive Middleware for Scientific Cyberinfrastructure

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Cyberenvironments: Adaptive Middleware for Scientific Cyberinfrastructure

ARM’07

Jim Myers, Bob McGrath

jimmyers@ncsa.uiuc.edu

National Center for Supercomputing Applications (NCSA),

University of Illinois at Urbana-Champaign

National Center for Supercomputing Applications


Outline

  • What’s Changing in Science?

  • What Role should Cyberinfrastructure (CI) play?

  • Requirements and Design for Cyberenvironments: Adaptive/Reflective Techniques

  • Some Examples

  • Conclusions

National Center for Supercomputing Applications


How is Science Changing?

  • Quantitative Modeling and Simulation

  • Better Data (e.g. Higher Signal to Noise)

  • More Data (e.g. High Throughput)

    • Closer ties between research and application

    • Investigation of subtle, non-linear, multi-dimensional phenomena

    • Statistical analysis of complex systems

National Center for Supercomputing Applications


Dq2

Valid Range

Dq1

Supporting the Research Lifecycle…

Standards /

Best practice

Algorithms/

Services

Reference Data

Apply

Curate

Engineering Views

Gap

Analysis

Analyze

Publish

Provenance

Annotation

Experiment Design

Project Execution

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‘Amdahl’s Law’ for Scientific Progress

!

Data production

Processing power

Data transfer/storage

Data discovery

Translation

Experiment setup

Group coordination

Tool integration

Training

Feature Extraction

Data interpretation

Acceptance of new models/tools

Dissemination of best practices

Interdisciplinary communication

National Center for Supercomputing Applications


CI versus the Literature/Out of Band Processes?

  • Higher Fidelity, Multiple Levels of Description

  • Custom Views

  • Actionable, Faster, Automatable

  • But software is rigid relative to text…

    • CI must be built before the parts are done

    • It must be evolvable by independent parties

    • It must enable coordination without central control

    • It must allow science to evolve / progress (no fixed domain model)

    • Researchers/educators must be able to work in multiple communities/value chains (across CI projects)

    • It must convey knowledge as well as tools to end users

    • It must align the interests of CI funders, developers, providers, users, …

National Center for Supercomputing Applications


Key Cyberenvironment Design Concepts

  • Explicit Separation of How from What:

    • Content (type, global IDs, …) and Conceptual Context (metadata…)

    • Process (workflow, provenance, …)

    • Virtual Organizations/Social Networks (policies, resources, semantics, translation)

    • GUI Integration (portals, rich clients, …)

  • Ability to pass information through components that don’t understand the details (everything is data)…

    …e-Science, Semantic Grid, Cyberenvironments, Web 2.0 …

    …intelligence at the edges…

National Center for Supercomputing Applications


Mid-America Earthquake Center

Examples: MAEViz(Consequence-Based Risk Management for Seismic Events)

Decision

Support

Damage

Prediction

Fragility

Models

Inventory

Selection

  • Engineering View of MAE Center Research

  • Portal-based Collaboration Environment

  • Distributed Data/metadata Sources

  • Multi-disciplinary Collaboration

Hazard

Definition

National Center for Supercomputing Applications


Examples: CyberIntegrator

  • Exploratory workflow (macro-recording)

  • Simple integration with Matlab, Excel, Fortran, etc.

  • Provenance tracking

  • Distributed, shared data access (HIS, WebDAV, …)

  • Remote Execution

  • Workflow/model publication

  • Metadata and Annotation of data, modules, workflows

National Center for Supercomputing Applications


Examples: CyberCollaboratory Portal

  • Group Spaces

  • Library, discussion, announcements, wiki, …

  • Simplified invitation

  • Email integration

  • Provenance tracking/social network analysis

National Center for Supercomputing Applications


Content & VO Aware

Secure

Enterprise

Data

Desktop

Public

Reference

Data

Data/Metadata

Check VO and

personal preferences

Translate

Virtual

Data

(from Recipes)

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Process Aware

Publish/

Discover

Process

Capture

Execute

Retrieve Data

Retrieve Code

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Dynamic

New Third-Party

Analyses (Forms, Visualizations)

Compare, Contrast,

Validate

Auto-update

MAEviz

GIS

Workflow

Data

Eclipse RCP

Plug-in Framework

National Center for Supercomputing Applications


Social/Conceptual Context

  • Capture of Interactions in Portal and in the Literature

  • Capture of Annotations/Associations

  • Provide Browsing and Recommender Interfaces

National Center for Supercomputing Applications


What do CyberEnvironments/CI for scientific discourse have to do with ARM?

  • Thesis: the principles of ARM are critical design patterns for viable CEs

    • Abstract services

      • NSF CI, Grid—resource management, authentication, etc.

      • Support for science process (e.g., virtual organizations)

      • RCP and other component frameworks for composing software

    • Expose metadata

      • Generic content management

      • Generic process management

      • Open metadata using RDF

    • Instrumentation

      • Universal capture of provenance, annotation

National Center for Supercomputing Applications


A Reflective Model

  • VO manager separate from App and CI developers

  • Can move from local to grid/web solutions w/o app changes

  • Semantic middleware as scalable communication layer…

  • Open Provenance Model, FOAF, DC, … as common conventions

What needs to be done

Which component(s) can do the work?

What does the component need to know?

Where can the information be found?

What can the component add to the story?

National Center for Supercomputing Applications


Conclusions

  • Building Cyberenvironments/supporting Scientific Discourse is critical for scientific efficiency/competitiveness.

  • Abstract management of data, process/provenance, social, and conceptual contexts solves real socio-technical problems in science and engineering research.

  • Our experience in building Cyberenvironments on these principles is showing their potential in terms of supporting systems science and evolving research.

  • E-Science, semantic web/grid, content management, Web 2.0 are all driving in this direction, but their impact is not well stated in terms of value to science researchers.

National Center for Supercomputing Applications


Acknowledgments

The authors wish to acknowledge the contribution of many CI researchers to the concepts and systems discussed here with specific recognition of members of NCSA’s Cyberenvironments Directorate.

The National Center for Supercomputing Applications is funded by the US National Science Foundation under Grant No. SCI-0438712. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

National Center for Supercomputing Applications


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