Generic e-Science Research

Henri Balbal@cs.vu.nl

Vrije Universiteit Amsterdam

Workflow

e-Science

Virtual Laboratory for e-Science (VL-e)

New e-Science plans (FES 2009)

Generic e-Science research in FES 2009

Introduction

● System level science● the integration of diverse sources of knowledge about the

constituent parts of a complex system with the goal of obtaining an understanding of the system's properties as a whole [Ian Foster]

● Multidisciplinary research● Each discipline can solve only part of a problem● Collaborations betweens distributed research

groups

● Research driven by (distributed) data● Data explosion, both volume and complexity

Examples

● Functioning of the cell for system biology● Cognition● Cancer research● Cohort studies in medicine (biobanking)● Discovery of biomarkers for drug design● Ecosystems/biodiversity● Studies of water/air pollution● Study black matter

e-Science

● Goal: allow scientists to collaborate in experiments and integration of research● Enable system level science

● Design methods to optimally exploit underlying infrastructure● Hardware (network, computing, datastorage)● Software (web, grid middleware)

e-Science in context

System levelexperiments

e-Science

Infrastructure

Web/grid software

Virtual Laboratory fore-Science (VL-e)

● Generic application support● Application cases are drivers for computer

science● Rationalization of experimental process● Midterm review:

● ``e-Science that works; gives Grid its correct role’’

Some VL-e results● Virtual Resource Browser:

● Integration platform for different applications

● Mirage & Virtual Lab for Medical Imaging:● Used autonomously at AMC for large-scale experiments

● EcoGRID:● Species observation records from many organizations

● Virtual Lab for Bird Migration Modeling:● Access military/meteorological radars & weather forecasts

● Ibis:● Programming/deployment of large-scale grid applications

Dynamicbird behaviour

MODELS

Bird distributionsEnsembles

Calibration andData assimilation

Predictions and on-linewarnings

RADAR

Bird behaviour in relation to weather and landscape

Example of generic approach

SCALE 2008 DACH 2008 - BS DACH 2008 - FT

AAAI-VC 2007 ISWC 2008

Multimedia

Computing

Astronomy

Semantic Web

● Runs simultaneously on clusters (DAS-3, Japan, Australia), Desktop Grid, Amazon EC2 Cloud

● Connectivity problems solved automatically by Ibis SmartSockets

Multimedia Content Analysis

Client

Broker

Servers IbisIbis

(Java)

eyeDentify● Object recognition on an Android

smartphone● Smartphone is a limited device:

● Can run only 64 x 48 pixels (memory bound)● 1024 x 768 pixels would take 5 minutes

● Distributed Ibis version:

+ =+1024 x 768 pixels

2.0 seconds

Outline

● e-Science● Virtual Laboratory for e-Science (VL-e)● New e-Science plans (FES 2009)

● Part of ICTregie FES proposal COMICT● Connecting, Mastering complexity, and Innovating by

Cooperation

● Generic e-Science research in FES 2009

Research questions

● How can we design, develop and build an adaptive e-Science environment that in a flexible way enables global collaboration in key areas of science?

● How can we establish an e-Science environment that is capable of handling the data explosion?

● How can we manage complexity via integration, at the application level and the generic e-Science level?

e-Science application projects

● e-Food & Flowers: WUR/VU (Top), TIFN, TIGG● e-BioScience & Life Sciences: UvA (Breit), RIVM● e-Biobanks: LUMC (Kok), AMOLF, AMC, Philips,

Schering-Plough● e-COAST & analytical science: TI-COAST (vd

Brink)● e-Ecology: UvA (Bouten), GAN● e-Data-intensive sciences: Nikhef (Templon),

RUG

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Two “real-life” environments

● e-Food● With TI Food and Nutrition (TIFN), TI Green Genetics

(TIGG), NBIC● e-Biobanking

● With Pearl String Initiative (Parelsnoer), Biobanking and Bio-molecular Resources Research Infrastructure(BBMRI-NL) and NBIC

● Under umbrella Netherlands Genomics Initiative (NGI) ● FES2009 application Life Sciences & Health

Generic e-Science

Panel VL-e workshop (29 Oct 2008)

Generic e-Science projects

I. Scientific data management

II. Information- and knowledge-management

III. Visualization

IV. Computing and resourcemanagement

V. e-Science infrastructureengineering

VI. Workflow management &application integration

VII. Reliability and security

Scientific data management

CWI/UvA (Kersten), RUG (Valentijn)

● Support large array-data (using MonetDB)● Multi-scale query execution

● With increasing precision more and more data in the warehouse is used to answers queries

● Astrosensor warehouse● Data lineage (back tracing to origin of data)

Information- and knowledge-management

VU (van Harmelen, Schreiber),UvA (Adriaanse, Marshall)

● Robust & large scale techniques for accessing & reasoning over distributed data-sources

● Tools for integration of data with scientific publications; data provenance, lineage, trust

● Tools for data-sharing: entity naming, semantic enrichment, interlinking acrosssemantically heterogeneous vocabularies

Large Scale Data Visualization

CWI (van Liere), UvA (Belleman), SARA (Berg)

● Knowledge Assisted Feature Visualization● How to provide semantic meaningful interactive

visualizations for very large and complex data?

● User Driven Exploratory Visual Analysis● If automatic analysis fails

● Applications: mass spectrometry, CFD, HEP, CosmoGrid, Ijkdijk, …

Computing & resource management

VU (Bal, Seinstra), TU Delft (Epema)

● Map e-Science applications onto hybrid systems, optimize performance & energy● DAS-4: Multicores, GPUs, FPGAs,

MPSoCs (Cell/BE)

● Scheduling algorithms supporting co-allocation of compute-, data-, and network-sources

● Builds on Ibis & KOALA software● Many app’s (VUMC, AMOLF, MultimediaN,

astronomy)

e-Science infrastructure engineering

UvA (de Laat), VU (Bal), TUD (Langendoen), TNO (Meijer)

● Resource information system based on Semantic Web and RDF (Resource Description Framework)

● Highly mobile data sensors

● User Programmable Networks

“I want” approach

contentcontent

RDF/CGRDF/CG

RDF/ST

RDF/NDL

RDF/NDL

RDF/VIZ

RDF/CPU

Application: find video containing x,then trans-code to it view on Tiled Display

PG&CdLQuickTime™ and a

TIFF (LZW) decompressorare needed to see this picture.

Workflow management &application integration

UvA (Bubak, Belloum), VU (Kielmann)

● Improve interoperability, sustainability and platform convergence in Scientific Workflow● Define a shared “standard”

for workflow metadata ● Workflow provenance models

● Make middleware-independent APIs for applications & programming environments● Cf. JavaGAT, SAGA, XtreemOS

Reliability and securityNIKHEF (Groep), UvA (van ‘t

Noordende),VU (Fokkink), TU Twente (van de Pol),

Logica (Mulder), SARA (Bouwhuis)● Improve availability, stability, and reliability

of the infrastructure● Monitoring, failure analysis● Self-healing

● Use formal verification techniques● Large-scale Model Checking becomes feasible on

grids (as shown on wide-area DAS-3)● Provide security

● Security policy enforcement, auditing

Summary

● Real-life application environments for e-Food and e-Biobanking

● New partners (TI-COAST, RUG, UT, ….) and new groups (Kersten, van Harmelen, Langendoen, Fokkink, vd Pol, ….)

● DAS-4● Many new research topics

Questions?