Managing Variability in Workflow with Feature Model Composition Operators

Managing Variability in Workflow with Feature Model Composition Operators

Mathieu Acher1, Philippe Collet1, Philippe Lahire1, Robert France2

1 University of Nice Sophia Antipolis (France),

Modalis Team (CNRS, I3S Laboratory)

2 Computer Science Department,

Colorado State University

Context: Medical Imaging Services

• Scientists build workflows by assembling a wide variety of medical imaging algorithms

– Processing chain to manipulate large medical data sets

Managing Variability in Workflow with Feature Model Composition Operators 2









Context: Medical Imaging Grid Services


– processing chain to manipulate large medical data sets

• Requirement: deployable on Grid infrastructures

– High throughput, performance computing, data storage, resource federation


Context: Medical Imaging Grid Services


– processing chain to manipulate large medical data sets

• Requirement: deployable on Grid infrastructures

– High throughput, performance computing, data storage, resource federation

• Services embed the business code and are invoked remotely through standardized protocol and well-defined interfaces


Services: A lot of Concerns• Highly Parameterized Services

– efficiently extend, change, customize, or configure services from various perspectives for use in a particular context


Medical Image Format: DICOM?Nifti?Analyze?

Modality Acquisition:MRI? CT? PET?

Algorithm: User intervention? Method (spatial? frequency?)Transformation (Linear? Affine?)







Network :Protocol? Format?

Grid Deployment: Operation System? Library ? Processor required?

Security: Data? Level Access (VO)?







Network :Protocol? Format?

Grid Deployment: Operation System? Library ? Processor required?

Security: Data? Level Access (VO)?

Services as Software Product Lines• Highly Parameterized Services

– efficiently extend, change, customize, or configure services for from various perspectives use in a particular context

• Service as software product line (SPL)• See papers SOAPL’08, MICCAI-Grid’08

• Managing variability within services

– to enhance reusability

• …and across services– to enhance composability


Network Protocol

Concern

Medical Image

Concern

Grid Deployment

Concern

Variability in Workflow

Managing the variability

in the entire processing chain



Managing the



Scalability? 40 features per Service, 360 features for the workflow…

Overview of the Approach


Overview of the Approach (2)

• Considering multiple dimensions of a service

– And their variation points

• Reasoning about concerns with variability

– To ensure consistency of services composed

• Assisting the user during configuration

– To select right variants

• … Dealing with the entire workflow…


Overview of the Approach (3)

• Modeling – Workflow

– Services

– Dependency

– Concerns with Variability

• Composition Operators to reason about variable concerns and assist the user in selecting variants for the entire worflow


Metamodel of Workflow and Service


Dataport

FunctionalInterface

Parameter

Service

1

-parameters1..*

1

-output 1..*

1

-input 1..*

1

-operations1..*

DeploymentInformation

GridDeploymentInformation

-uri : String

ComputingNode

1

-nodes1

1

-deployment

1

Workflow

-id : String

Process-id : String

Relation

-value : Boolean

Guard

1

-processors1..* -relations0..*

-name : String

Variable

-left

1-right

1

-condition

1

-realizedBy

11

Source Sink

Metamodel of Workflow and Service


Source

Intensity

Correction

Segmentation Sink

Unbias Sink

Service

Element

Dataport

FunctionalInterface

Parameter

Service

1

-parameters1..*

1

-output 1..*

1

-input 1..*

1

-operations1..*

DeploymentInformation

GridDeploymentInformation

-uri : String

ComputingNode

1

-nodes1

1

-deployment

1

Workflow

-id : String

Process-id : String

Relation

-value : Boolean

Guard

1

-processors1..* -relations0..*

-name : String

Variable

-left

1-right

1

-condition

1

-realizedBy

11

Source Sink

Modeling Weaving of Concern


Weaving of Variable Concern


FService1

Output Dataport

Modality

AcquisitionFormat

Medical

Image

T1 T2DICOM

FMo1

Anonymized

Instance of Joinpoint

Weaving

Concerns as Feature Models

• Hierarchy + Variability

– Mandatory features, Optional features

– Alternatives and Constraints


AnonymizedFormat

DICOM Nifti Analyze

Modality Acquisition

MRI CT SPEC

T1 T2

PET

Medical Image

And-Group

Optional

Mandatory

Xor-Group

Or-Group

Concerns as Feature Models

• Hierarchy + Variability

– Mandatory features, Optional features

– Alternatives and Constraints


AnonymizedFormat

DICOM Nifti Analyze

Modality Acquisition

MRI CT SPEC

T1 T2

PET

Medical Image

And-Group

Optional

Mandatory

Xor-Group

Or-Group

Variable Concern Dependency Modeling


FService1

Output Dataport

FMi2

FService2

Input Dataport


Weaving

FMo1



FService1

Output Dataport

FMi2

FService2

Input Dataport


Weaving

FMo1



FService1

Output Dataport

FMi2

FService2

Input Dataport


Weaving

FMo1

Functional Interface Functional Interface

FMfi1FMfi2

Grid Node Grid Node

FMg1

FMg2

Reasoning: Merge Operator


FService1

Output Dataport

NiftiDICOM

Modality

AcquisitionFormat

Medical

Image

T1 T2

FMi2

FService2

Input Dataport

Modality

AcquisitionFormat

Medical

Image

T1 T2DICOM

FMo1

Anonymized


Weaving

When two feature models (FMs) share several features, there is a need to merge the overlapping parts.

Composing Feature Models (SLE’09)Comparing Approaches to Implement Feature Model Composition (ECMFA’10)

Feature Model Operator in Action


+∩

FService1

Output Dataport

NiftiDICOM

Modality

AcquisitionFormat

Medical

Image

T1 T2

FMi2

FService2

Input Dataport

Modality

AcquisitionFormat

Medical

Image

T1 T2DICOM

FMo1

Anonymized

(intersection)

FMr

Modality

AcquisitionFormat

DICOM

Medical

Image

T1 T2


Weaving

Configurating and Propagating Choices


Feature Model Operator in Action (2)


+∩

FB

A

DC

FMep1

G H

I J

FMep2

FB

A

D EC G H

I

FMep3

FB

A

M E G H

I J

+∩

+∩

FB

A

DC G H

I

FMep'2

FB

A

E G H

I

FMep'3

error

FMo1FMi2 FMi3

FMepn

FMinFMo2 FMo3

FService1

Functional Interface

Input

Dataport

Output

Dataport

FService2


Input

Dataport

Output

Dataport

FService3


Input

Dataport

Output

Dataport

FMi1

FServicen


Input

Dataport

Output

Dataport

FMon

Concurrency


FService1

Output Dataport

FService2

Input Dataport

FService3

Input Dataport

FMi2

FMi3

FMo1

FServicen

Input Dataport

FMin

Modality

AcquisitionFormat

Medical

Image

T1 T2DICOM

FMo1

Anonymized

NiftiDICOM

Modality

AcquisitionFormat

Medical

Image

T1 T2

FMi2

FMi3

Modality

AcquisitionFormat

DICOM

Medical

Image

T1 T2

FMin

Modality

AcquisitionFormat

DICOM

Medical

Image

T1 T2Analyze

Concurrency


FService1

Output Dataport

FService2

Input Dataport

FService3

Input Dataport

FMi2

FMi3

FMo1

FServicen

Input Dataport

FMin

Modality

AcquisitionFormat

Medical

Image

T1 T2DICOM

FMo1

Anonymized

NiftiDICOM

Modality

AcquisitionFormat

Medical

Image

T1 T2

FMi2

FMi3

Modality

AcquisitionFormat

DICOM

Medical

Image

T1 T2

FMin

Modality

AcquisitionFormat

DICOM

Medical

Image

T1 T2Analyze

Xor

Condition


FService3


FMep3

FService2


FMep2

FService1


FMep1

Guard FService4


FMep4

Condition


FService3


FMep3

FService2


FMep2

FService1


FMep1

Guard FService4


FMep4

Condition


FService3


FMep3

FService2


FMep2

FService1


FMep1

Guard FService4


FMep4

Condition


FService3


FMep3

FService2


FMep2

FService1


FMep1

Guard FService4


FMep4

“Union” of the two paths

Merge Operator: Union


FM2T2

MRI

T1FM1

T2

MRI

T1

{{MRI, T1}, {MRI, T1, T2}}

{{MRI, T1}, {MRI, T2}}

{{MRI, T1},{MRI, T1, T2},{MRI, T2}}

T2

MRI

T1

Benefits

• Automated detection of inconsistency at design or at configuration time

• Separation of Concerns– locate the source of errors and give information to

assist users

– incremental and independent reasoning

• The merge contributes to decrease the number of remaining variability choices.– can reduce the amount of time and effort needed

during the configuration process



Managing the



Scalability? 40 features per Service, 360 features for the workflow…

Prototype Implementation

• http://modalis.polytech.unice.fr/softwares/manvarwor

• We provide:– Ecore metamodels of workflow, service, joinpoint

– a Domain-Specific Language (DSL) to specify where the variable concerns are attached to specific joinpoint into a workflow model

– a DSL to design variable concerns and feature models

• Realization in Kermeta– Dependencies analysis between variable concerns at the workflow level

– Reasoning about variable concerns at the feature model level


http://modalis.polytech.unice.fr/softwares/manvarwor

Conclusion

• Separation of Concerns for managing highly parameterized services in scientific workflow.

– (1) Modeling workflows, services, (variable) concerns and their dependencies

– (2) Composing and configuring services and associated concerns boils down to composing feature models

– (3) Feature models composition operator are used on different constructions of workflow


Future Work• Dealing with feature model constraints

– relations can exist between variables concerns of a service / two services

• Aligning Feature Models

• Generalization of the approach to any (workflow or service) metamodel

• Evaluation on the case study

– practical use of the approach

– scalability on large workflows


?

Education

Managing Variability in Workflow with Feature Model Composition Operators