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Conservation of Computational Scientific Execution
Environments for Workflow-based Experiments Using
Ontologies
Date: 22/01/16
Idafen Santana-Pérez
Supervisors: María S. Pérez-Hernández, Oscar Corcho
Introduction
2Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
HYPOTHESIS CONVINCEAUDIENCE
REPEATABLE
SCIENTIFIC EXPERIMENTS
Introduction
3Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
SCIENTIFIC EXPERIMENTS
IN VIVO/VITRO IN SILICO
Introduction
4Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
SCIENTIFIC EXPERIMENTS
IN VIVO/VITRO IN SILICO
REPEATABILITY
Terminology
PRESERVATION
CONSERVATION
5Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Terminology
PRESERVATION
CONSERVATION
REPLICABILITY
REPRODUCIBILITY
6Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Experiment components
7Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
DATA SCIENTIFIC PROCEDURE EQUIPMENT
IN V
IVO
/VIT
RO
IN S
ILIC
O
Experiment components
8Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
DATA SCIENTIFIC PROCEDURE EQUIPMENT
IN V
IVO
/VIT
RO
IN S
ILIC
O
Experiment components
9Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
DATA SCIENTIFIC PROCEDURE EQUIPMENT
IN S
ILIC
O
Experiment components
10Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
DATA SCIENTIFIC PROCEDURE EQUIPMENT
IN S
ILIC
O
Experiment components
11Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
DATA SCIENTIFIC PROCEDURE EQUIPMENT
IN S
ILIC
O
Experiment components
12Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
DATA SCIENTIFIC PROCEDURE EQUIPMENT
IN S
ILIC
O
Research Methodology
13Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
State of the Art
Open Research Problems
Hypothesis
& GoalsEvaluation
Open Research Problems
14Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Open Research Problems
15Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Computational Infrastructures are usually a predefined element of a Computational Scientific Workflow.
Open Research Problems
16Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Computational Infrastructures are usually a predefined element of a Computational Scientific Workflow.
• Execution Environments are poorly described.
Open Research Problems
17Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Computational Infrastructures are usually a predefined element of a Computational Scientific Workflow.
• Execution Environments are poorly described.
• Current reproducibility approaches for computational experiments consider only data and procedure.
Outline
18Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
1. Introduction and motivation
2. Hypothesis and goals
3. Execution environment representation
4. Experiment reproduction
5. Evaluation
6. Conclusions and future work
Hypothesis
19Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
It is possible to describe the main properties of the Execution Environment of a Computational Scientific Experiment and, based on this description, derive a reproduction process for generating an equivalent environment using virtualization
techniques.
Hypothesis
20Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
It is possible to describe the main properties of the Execution Environment of a Computational Scientific Experiment and, based on this description, derive a reproduction process for generating an equivalent environment using virtualization
techniques.
• Hypothesis 1: Semantic technologies are expressive enough to describe the Execution Environment of a Computational Scientific Experiment.
Hypothesis
21Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
It is possible to describe the main properties of the Execution Environment of a Computational Scientific Experiment and,
based on this description, derive a reproduction process for generating an equivalent environment using virtualization
techniques.
• Hypothesis 2: An algorithmic process can be developed that, based on the description of the main capabilities of an Execution Environment, is able to define an equivalent infrastructure for executing the original Computational Scientific Experiment obtaining equivalent results.
Hypothesis
22Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
It is possible to describe the main properties of the Execution Environment of a Computational Scientific Experiment and, based on this description, derive a reproduction process for generating an equivalent environment using virtualization
techniques.
• Hypothesis 3: Virtualization techniques are capable of supporting the reproduction of an Execution Environment by creating and customizing computational resources, such as Virtual Machines, that fulfil the requirements of the former experiment.
Goals
23Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Goal 1: Create a model able to conceptualize the set of relevant capabilities that describe a Computational Infrastructure.
• Goal 2: Design a framework to provide means for populating these models, collecting information from the materials of a Computational Scientific Experiment and generating structured information.
H1
H1
Goals
24Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Goal 3: Propose an algorithm that, based on the description of a former Computational Infrastructure, is able to define an equivalent infrastructure specification.
• Goal 4: Integrate a system able to deploy virtual machines on several Virtualized Infrastructure providers, meeting a certain hardware specification and install and configure the proper software stack, based on the deployment plan specified by the aforementioned algorithms.
H2
H3
Restrictions and assumptions
25Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Restrictions • Performance• Common software components• Web services• Data-related aspects
• Assumptions• Reproducibility is more important than performance• Sc. Workflows are a widely accepted approach• Virtualization solutions are a mature technology• Equivalent environment and results
Outline
26Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
1. Introduction and motivation
2. Hypothesis and goals
3. Execution environment representation
4. Experiment reproduction
5. Evaluation
6. Conclusions and future work
Representation
27Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
CLOUD
• Describing execution environments
FORMEREQUIPMENT
ANNOTATE REPRODUCE
SEMANTIC ANNOTATIONS
EQUIVALENT EXECUTION
ENVIRONMENT
Representation
• Semantic models for describing the main aspects related to the execution of a workflow.• Workflow• Software• Hardware• Computational resources
• Increasing the understanding of the underlying components
• Making this knowledge explicit• Easy to extend and integrate
• NeOn methodology• Scenario-based methodology for building ontologies
• Standard technology: RDF & OWL
28Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Representation
• WICUS ontology network • Workflow Infrastructure Conservation Using Semantics• http://purl.org/net/wicus• 5 ontologies• WICUS Workflow Execution Requirements ontology• WICUS Software Stack ontology• WICUS Hardware Specs ontology• WICUS Scientific Virtual Appliance ontology• WICUS Ontology: links the previous ontologies
29Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
WICUS ontology network
• WICUS Workflow Execution Requirements ontology• http://purl.org/net/wicus-reqs
30Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
WICUS ontology network
• WICUS Software Stack ontology• http://purl.org/net/wicus-stack
31Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
WICUS ontology network
• WICUS Scientific Virtual Appliance ontology• http://purl.org/net/wicus-sva
32Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
WICUS ontology network
• WICUS Hardware Specs ontology• http://purl.org/net/wicus-hwspecs
33Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
WICUS ontology network
• WICUS ontology network• http://purl.org/net/wicus
34Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
WICUS ontology network
• WICUS ontology network• http://purl.org/net/wicus
35Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Outline
36Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
1. Introduction and motivation
2. Hypothesis and goals
3. Execution environment representation
4. Experiment reproductionA. Parsing tools and semantic annotations
B. Specification process
C. Enactment and execution
5. Evaluation
6. Conclusions and future work
WICUS system
• Overview, inputs and outputs
37Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Outline
38Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
1. Introduction and motivation
2. Hypothesis and goals
3. Execution environment representation
4. Experiment reproductionA. Parsing tools and semantic annotations
B. Specification process
C. Enactment and execution
5. Evaluation
6. Conclusions and future work
Parsing tools and semantic annotations
39Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Parsing tools and semantic annotations
40Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Parsing tools and semantic annotations
• Workflow Specification File
41Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Parsing tools and semantic annotations
• Workflow Parser and Annotator• Workflow Annotations
42Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Parsing tools and semantic annotations
• WMS Annotations
43Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Parsing tools and semantic annotations
• Software Components Registry
44Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Parsing tools and semantic annotations
• Software Components Annotator• Software Components Catalog
45Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Parsing tools and semantic annotations
• Software Components Annotator• Software Components Catalog• Workflow & Configuration Annotations
46Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Parsing tools and semantic annotations
• Scientific Virtual Appliance Catalog
47Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Outline
48Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
1. Introduction and motivation
2. Hypothesis and goals
3. Execution environment representation
4. Experiment reproductionA. Parsing tools and semantic annotations
B. Specification process
C. Enactment and execution
5. Evaluation
6. Conclusions and future work
Specification process
49Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Specification process
• Infrastructure Specification Algorithm (ISA)
50Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
GET WFREQUIREMENTS
GET<REQ,STACKS>
GET<REQ,D-GRAPH>
GET AVAILABLESVA
GET<SVA,STACKS>
CALCULATEREQ-SVA
COMPATIBILITY
GET MAX COMPATIBLE
REQ-SVA
CLEAN REQD-GRAPH
Infrastructure Specification Algorithm
WORKFLOW
REQ1
REQ2
REQ3
51Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
WORKFLOW
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
52Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
WORKFLOW
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
S15
53Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
WORKFLOW
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
SVA1
SVA2
SVA3
S15
54Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
WORKFLOW
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
SVA1
SVA2
SVA3
S13
S15
S15
S14
S14
55Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
WORKFLOW
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
SVA1
SVA2
SVA3
S13
S15
S15
S14
S14
56Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
WORKFLOW
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
SVA1
SVA2
SVA3
S13
S15
S15
S14
S14
57Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
WORKFLOW
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
SVA1
SVA2
SVA3
S13
S15
S15
S14
S14
58Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
SVA1
SVA3
S13
S15S15
S14
SVA3S15
59Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
SVA1
SVA3
S13
S15S15
S14
SVA3S15
60Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
S15
S13
S14
SVA1
SVA3
S13
S15S15
S14
SVA3S15
61Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Infrastructure Specification Algorithm
REQ1
REQ2
REQ3
S1
S2
S3
S4
S5
S6
S7
S8
S9
S10
S11
S12
SVA1
SVA3
S13
S15
S14
SVA3S15
62Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Specification process
• Abstract Deployment Plan• Provider-independent representation format• Based on the WICUS stack ontology
63Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Outline
64Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
1. Introduction and motivation
2. Hypothesis and goals
3. Execution environment representation
4. Experiment reproductionA. Parsing tools and semantic annotations
B. Specification process
C. Enactment and execution
5. Evaluation
6. Conclusions and future work
Enactment and Execution
65Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Enactment and Execution
• PRECIP• Pegasus Repeatable Experiments for the Cloud in
Python (PRECIP)• API for running experiments in Clouds• OpenStack and AWS EC2 API• Running remote commands and file transfer• No pre-installed components in the VM images
66Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Enactment and Execution
67Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Vagrant• Local virtualization• Virtualization tools
• VirtualBox• VMWare
• Vagrantfiles• Shared folder
Summary
68Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Outline
69Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
1. Introduction and motivation
2. Hypothesis and goals
3. Execution environment representation
4. Experiment reproduction
5. Evaluation
6. Conclusions and future work
Evaluation
• Workflows reproduced
70Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Evaluation
• Workflows reproduced• 3 scientific domains
71Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
Evaluation
• Workflows reproduced• 3 scientific domains• 3 workflow management systems
72Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
Evaluation
• Workflows reproduced• 3 scientific domains• 3 workflow management systems• 6 different workflows
73Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
(2003) (2014)(2014) (2015) (2011)(2011)
Evaluation
• Experimental setup
74Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
AWS EC2 FutureGrid Vagrant
• Public Cloud provider• De facto standard
• Academic Cloud facility• OpenStack Havana• India server
• 1024 cores • 3072 GB RM
• Local virtualization solution
• VirtualBox• Ubuntu 12.04.5
• 4 cores, at 2 GHz • 8 Gb RAM
PRECIP VAGRANT
Evaluation
• Experimental setup
75Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Evaluation
76Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
Results
FORMEREQUIPMENT
ANNOTATE REPRODUCE
CLOUD
EQUIVALENT EXECUTION ENVIRONMENTSEMANTIC
ANNOTATIONS
COMPARE
Evaluation
77Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
Results
CLOUD
FORMEREQUIPMENT
ANNOTATE REPRODUCE
SEMANTIC ANNOTATIONS
EQUIVALENT EXECUTION ENVIRONMENT
COMPARE
Evaluation
78Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
Results
CLOUD
FORMEREQUIPMENT
ANNOTATE REPRODUCE
SEMANTIC ANNOTATIONS
EQUIVALENT EXECUTION ENVIRONMENT
COMPARE
• Non-deterministic• Standard and error output• Generated files equivalent
Evaluation
79Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
Results
CLOUD
FORMEREQUIPMENT
ANNOTATE REPRODUCE
SEMANTIC ANNOTATIONS
EQUIVALENT EXECUTION ENVIRONMENT
COMPARE
• Same results• Results from Int. Extinction
may vary
Evaluation
80Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
Results
CLOUD
FORMEREQUIPMENT
ANNOTATE REPRODUCE
SEMANTIC ANNOTATIONS
EQUIVALENT EXECUTION ENVIRONMENT
COMPARE
• Genomic data• Exact match
Evaluation
81Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Domain Seismic Astronomy Bio
WMS dispel4py Pegasus Makeflow
Name xcorr InternalExtinction Montage Epigenomics SoyKB BLAST
Results
CLOUD
FORMEREQUIPMENT
ANNOTATE REPRODUCE
SEMANTIC ANNOTATIONS
EQUIVALENT EXECUTION ENVIRONMENT
COMPARE
Outline
82Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
1. Introduction and motivation
2. Hypothesis and goals
3. Execution environment representation
4. Experiment reproduction
5. Evaluation
6. Conclusions and future work
Conclusions
83Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Hypothesis 1: Semantic technologies are expressive enough to describe the Execution Environment of a
Computational Scientific Experiment.
• Goal 1• WICUS ontology network
• Goal 2• Parsing and annotations modules
Conclusions
84Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Hypothesis 2: An algorithmic process can be developed that, based on the description of the main capabilities of an Execution Environment, is able to define an equivalent infrastructure for executing the original Computational
Scientific Experiment obtaining equivalent results
• Goal 3• Infrastructure Specification Algorithm• Abstract Deployment Plan
Conclusions
85Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Hypothesis 3: Virtualization techniques are capable of supporting the reproduction of an Execution Environment by creating and customizing computational resources, such as Virtual Machines, that fulfil the requirements of the former
experiment.
• Goal 4• Script Generator for PRECIP and Vagrant scripts• AWS EC2, FutureGrid, and Vagrant
Conclusions
• Other approaches• Sharing VM• Exhaustive trace of the execution components• Semantic description for business processes
86Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Dissemination
87Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Journals
• Idafen Santana-Perez, Rafael Ferreira da Silva, Mats Rynge, Ewa Deelman, María S. Pérez-Hernández, Oscar Corcho, “Reproducibility of execution environments in computational science using Semantics and Clouds”, Future Generation Computer Systems, Available online 8 January 2016, ISSN 0167-739X, http://dx.doi.org/10.1016/j.future.2015.12.017 (impact factor: 2.786)
• Santana-Perez, Idafen and Pérez-Hernández, María , “Towards Reproducibility in Scientific Workflows: An Infrastructure-Based Approach” Scientific Programming, vol. 2015, Article ID 243180, 11 pages, 2015. doi:10.1155/2015/243180 (impact factor: 0.559)
Dissemination
88Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Conferences & workshops
• Doug James, et. al. (including Santana-Perez, Idafen),“Standing Together for Reproducibility in Large-Scale Computing: Report on reproducibility@XSEDE” reproducibility@XSEDE workshop, 2014.
• Santana-Perez, Idafen, Ferreira da Silva, Rafael, Rynge, Mats, Deelman, Ewa, Pérez-Henández, María, Corcho, Oscar , “A Semantic-Based Approach to Attain Reproducibility of Computational Environments in Scientific Workflows: A Case Study” 1st International Workshop on Reproducibility in Parallel Computing (REPPAR14) in conjunction with Euro-Par 2014 (August 25-29), Porto, Portugal.
• Santana-Perez, Idafen and Pérez-Hernández, María.; , “A Semantic Scheduler Architecture for Federated Hybrid Clouds” Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on , vol., no., pp.384-391, 24-29 June 2012.
Future work
89Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
Future work
90Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Incentives for scientists to produce reproducible results• Define roles and responsibilities• Infrastructure management plan
Future work
91Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Incentives for scientists to produce reproducible results• Define roles and responsibilities• Infrastructure management plan
• Publish descriptions as Linked Data• Linking it with other resources describing scientific
workflows
Future work
92Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Incentives for scientists to produce reproducible results• Define roles and responsibilities• Infrastructure management plan
• Publish descriptions as Linked Data• Linking it with other resources describing scientific
workflows
• Multi-node infrastructures
Future work
93Conservation of Computational Scientific Execution Environments for Workflow-based Experiments Using Ontologies
• Incentives for scientists to produce reproducible results• Define roles and responsibilities• Infrastructure management plan
• Publish descriptions as Linked Data• Linking it with other resources describing scientific
workflows
• Multi-node infrastructures
• Completeness of annotations
Conservation of Computational Scientific Execution
Environments for Workflow-based Experiments Using
OntologiesIdafen Santana-Pérez
Supervisors: María S. Pérez-Hernández, Oscar Corcho
Date: 22/01/16
Experimental materials available online:http://w3id.org/idafensp/ro/wicuspegasusmontagehttp://w3id.org/idafensp/ro/wicuspegasusepigenomicshttp://w3id.org/idafensp/ro/wicuspegasussoykbhttp://w3id.org/idafensp/ro/wicusdispel4pyastrohttp://w3id.org/idafensp/ro/wicusdispel4pyxcorrhttp://w3id.org/idafensp/ro/wicusmakeflowblast
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