A Semantic-Based Approach to Attain Reproducibility of Computational Environments in Scientific Workflows: A Case Study

A Semantic-Based Approach to Attain Reproducibility of

Computational Environments in Scientific Workflows: A Case Study

Idafen Santana-Perez1, Rafael Ferreira da Silva2, Mats Rynge2 Ewa Deelman2, María S. Pérez-Hernández1, Oscar Corcho1

1Ontology Engineering Group, Universidad Politécnica de Madrid, Madrid, Spain 2Univ. of Southern California, Information Sciences Institute, Marina Del Rey, CA, USA

REPPAR'14 1st International Workshop on Reproducibility in Parallel

Computing. Porto, August 2014

Index

•  Introduction •  Reproducibility tools

• WICUS •  Pegasus & PRECIP

•  Reproducibility process •  Annotation •  Infrastructure Specification Algorithm

•  Use case •  Conclusion & Future Work

2 A Semantic-Based Approach to Attain Reproducibility...

Introduction

•  Experiments in empirical science •  Primary component of the scientific method •  Main method for validating a hypothesis •  Repeatable procedure

•  Scientific publications •  Announce a result •  Convince readers that the result is correct

•  Computational science •  In silico science •  Computational scientific workflow: “a precise, executable

description of a scientific procedure” [De Roure, 2011]

•  “Reproducibility in principle underpins the scientific method” [Goble,2012]

•  “Its about capturing, preserving, reusing and curating” [Goble 2012]


Introduction

•  Reproducibility in Scientific Experiments


INPUT DATA SCIENTIFIC PROCEDURE EQUIPMENT

IN V

IVO

/VIT

RO

IN

SIL

ICO

Introduction



INPUT DATA SCIENTIFIC PROCEDURE EQUIPMENT

IN V

IVO

/VIT

RO

IN

SIL

ICO

CLOUD

Introduction



FORMER EQUIPMENT

ANNOTATE REPRODUCE

SEMANTIC ANNOTATIONS

EQUIVALENT EXECUTION

ENVIRONMENT


CLOUD

Semantics



FORMER EQUIPMENT

ANNOTATE REPRODUCE



ENVIRONMENT


Semantics

•  Vocabularies for documenting the main resources involved on the execution of a WF. •  Software •  Hardware •  Computational resources •  Workflow

•  Increasing the understanding of the underlying components

•  Making this knowledge explicit •  Standard technology: RDF & OWL •  Easy to extend and integrate


Semantics

•  WICUS ontology network •  Workflow Infrastructure Conservation Using Semantics •  http://purl.org/net/wicus •  5 ontologies •  WICUS Software Stack ontology •  WICUS Hardware Specs ontology •  WICUS Scientific Virtual Appliance ontology •  WICUS Workflow Execution Requirements ontology

•  WICUS Ontology: links the previous ontologies


WICUS ontology network

•  WICUS Software Stack ontology •  http://purl.org/net/wicus-stack



•  WICUS Hardware Specs ontology •  http://purl.org/net/wicus-hwspecs




•  WICUS Scientific Virtual Appliance ontology •  http://purl.org/net/wicus-sva


•  WICUS Workflow Execution Requirements ontology •  http://purl.org/net/wicus-reqs



•  WICUS ontology network •  http://purl.org/net/wicus


CLOUD

PRECIP



FORMER EQUIPMENT

ANNOTATE REPRODUCE



ENVIRONMENT


PRECIP

•  Pegasus Repeatable Experiments for the Clouds In Python

•  Experiment management control API •  Works with commercial and academic Clouds •  Tag-based system •  No need of pre-installed software on the VM image •  Create VM, transfer files, run commands remotely •  Linux


Pegasus

•  Pegasus WMS •  Million-task workflows •  Records

•  Data about execution •  Intermediate results

•  Replica Catalog •  DAX (Direct Acyclic Graph in XML) •  Transformation Catalog •  HTCondor for executing individual tasks


CLOUD

Reproducibility process



FORMER EQUIPMENT

ANNOTATE REPRODUCE



ENVIRONMENT




Pegasus Transf. Catalog

DAX xml DAX annotator

TC annotator

WF Annot

SW Comp

Catalog

WF & Config Annot

Inf. Spec. Algorithm

SVA Catalog

Precip Script

1

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WMS Annot

3


•  Infrastructure Specification Algorithm •  Goal: obtain an specification defining what VMs need to be

created, what software components must be deployed and their configuration.



•  Infrastructure Specification Algorithm


GET WF REQUIREMENTS

GET <REQ,STACKS>

GET <REQ,D-GRAPH>

GET AVAILABLE SVA

GET <SVA,STACKS>

CALCULATE REQ-SVA

COMPATIBILITY

GET MAX COMPATIBLE

REQ-SVA

CLEAN REQ D-GRAPH

Infrastructure Specification Algorithm




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Use case

•  Montage Workflow •  Astronomy workflow •  Construct large image mosaics of the sky •  Montage Software distribution •  59 binaries

•  Target IaaS Cloud Providers •  Amazon EC2 •  FutureGrid


RO available at http://pegasus.isi.edu/publications/reppar

Use case

•  Goal


AWS

MONTAGE WORKFLOW

ENVIRONMENT

ANNOTATE REPRODUCE



ENVIRONMENT

FG

Use case

•  Annotations


Use case

•  Annotations: Workflow


Use case

•  Annotations: Software


Use case

•  Annotations: C. Resources


Use case

•  Results •  2 PRECIP scripts (AWS and FG): creates VM, deploys and

configure software and executes the WF. •  Successfully executed •  Same results as the original WF


Conclusions

•  Semantic modelling approach to conserve computational resources

•  PRECIP for reproducing the execution environment on the Cloud

•  WICUS annotations + PRECIP scripting capabilities •  Apply those ideas to Montage on AWS EC2 and FG

•  Assume that the binaries are available


Future Work

•  Apply to other workflows •  Improve the annotation process •  Extend WICUS ontology network (new release coming

soon) •  Software variants •  Low level libraries •  Incompatibilities •  User policies


Questions

46 Conservation of Scientific Workflow Infrastructures by Using Semantics

Education

A Semantic-Based Approach to Attain Reproducibility of Computational Environments in Scientific Workflows: A Case Study