Managing Non-Functional Uncertainty via Model-Driven...

Leandro S. PintoPolitecnico di Milano, Italy

[email protected]

Paola SpoletiniUniversità dell’Insubria, [email protected]

Giordano Tamburrelli University of Lugano, Switzerland

[email protected]

Carlo Ghezzi Politecnico di Milano, Italy

[email protected]

Shonan Meeting, September 2013

Managing Non-Functional Uncertainty via Model-Driven Adaptivity

1

Friday, September 20, 13

mailto:[email protected]








A caveat

2 Shonan Meeting, September 2013


A caveat

Theory



A caveat

Theory Practice



A caveat

Theory Practice


First steps


Feedback

A caveat

Theory Practice


First steps


Motivation



Motivation

Complexity

Size Distribution Ownership



Motivation

Complexity

Size Distribution Ownership

Uncertainty

Execution Time Failure Rate



Adaptive Systems



Adaptive Systems

Adaptive Systems



Alternative Behaviors

Adaptive Systems

Adaptive Systems



Alternative Behaviors Exception Handling

Adaptive Systems

Adaptive Systems



An Example

5

The WPM: Apps should be responsive in less than three seconds or must display a visual progress or busy indicator



6

Our Goal



6

Our Goal

Formalize



6

Our Goal

Formalize Automate



7

ADAMAdaptive Model-driven execution



Models

7




Models

7


Transformation



Models

7


Probability TheoryTransformation



8

ADAMOverview



Activity Diagram

8

ADAMOverview

DeveloperUML Activity Diagrams Implementations

---------------------------------annotatesdesigns

System Modeling



generates

usesuses

Embedded Model

…………… …

Generator

Model Transformation

Activity Diagram

Markov Decision Process

8

ADAMOverview



System Modeling



generates

usesuses

Embedded Model

…………… …

Generator


Activity Diagram


Embedded Model

8

ADAMOverview



System Modeling

PRISM

usesModel Manipulation



generates

usesuses

Embedded Model

…………… …

Generator


Activity Diagram


Embedded Model

Interpreted Execution

8

ADAMOverview

System Execution

invokes

executes

Interpreter



System Modeling

PRISM

usesModel Manipulation



9

ADAMModeling



9

ADAMModeling

System is modeled as an annotated Activity Diagram:



9

ADAMModeling


0.3

2: Photo Acquisition 1: Input Price

5: Product Lookup

8: Web Search 7: Local Search

11: Publish Price

<<Optional>>10: ResultOrdering

3: Local Recognition

4: Remote Recognition

[hasAutoFocus]

<<Optional>>9: Secondary Web Search

0.6 0.4true false

6: Location

[recognized]

0.7truefalse



9

ADAMModeling


1. Optional Activities

0.3


5: Product Lookup


11: Publish Price




[hasAutoFocus]


0.6 0.4true false

6: Location

[recognized]

0.7truefalse



9

ADAMModeling



0.3


5: Product Lookup


11: Publish Price




[hasAutoFocus]


0.6 0.4true false

6: Location

[recognized]

0.7truefalse

2. Probabilistic Branches



9

ADAMModeling



0.3


5: Product Lookup


11: Publish Price




[hasAutoFocus]


0.6 0.4true false

6: Location

[recognized]

0.7truefalse

13: ShowMap

12: NPS

Location(NPS Alternative)

2. Probabilistic Branches3. Nested Activities



10

Description Metric Class

R1After an user input the

application shall respond in at most 3s.

Response Time (RT) Threshold

R2Maximize application

usability Usability (U) Max

R3Minimize battery

consumption Energy Consumption (E) Min

ADAMModeling



11

ADAMModeling



12

ADAMTransformation



12

ADAMTransformation

5: ProductLookupActivity



12

ADAMTransformation

5: ProductLookupActivity 5a

5b

MDP



12

ADAMTransformation

ActivityMDP

<<Optional>>9: Secondary WebSearch



12

ADAMTransformation

ActivityMDP

<<Optional>>9: Secondary WebSearch

9a

9b



12

ADAMTransformation

ActivityMDP

8: WebSearch<<Optional>>9: Secondary Web Search



12

ADAMTransformation

ActivityMDP


8 i

9a

9b



12

ADAMTransformation

ActivityMDP


8 i

9a

9b4

3

RT= 0.3sE=1U=1

10

7c g h

RT=0.6sE=2U=1

RT=0.5sE=2U=1

RT=0.3sE=1U=1

RT=0.6sE=1U=1

RT=0.8sE=2U=1

RT=2sE=3U=1

8 i

RT=0.6sE=2U=1

9a

9b

j

RT=1.6sE=2U=1

RT=1.5sE=2U=1

0.6

0.4

6a

12

eb

f 13

RT=0sE=1U=0

1 2a0.7

0.3

5a

5b

RT=0sE=1U=0

d

RT=0sE=1U=0

RT=0sE=1U=0

k 11

RT=0sE=2U=1



13

ADAMManipulation



13

ADAMManipulation

i

9a

9b

j

RT=1.6sE=2U=1

RT=1.5sE=2U=1



13

ADAMManipulation

i

9a

9b

j

RT=1.6sE=2U=1

RT=1.5sE=2U=1

9a

9b

i

RT=<0.5; 1.1>E=<4; 5>U=<1; 2>

RT=<0.6; 1.2>E=<4; 5>U=<1; 2>

j



14

ADAMExecution



14

ADAMExecution

Interpreter navigates the model and invokes implementations



14

ADAMExecution

Choice among alternative paths in the EM




14

ADAMExecution

Choice among alternative paths in the EM

Maximize likelihood of meeting system requirements




15

ADAMExecution



15

ADAMExecution

1

2

i

RT=<a; b>

j

RT=<a; b>

RT=<a; b>



15

ADAMExecution

cRT

1

2

i

RT=<a; b>

j

RT=<a; b>

RT=<a; b>

th



15

ADAMExecution

cRT

1

2

i

RT=<a; b>

j

RT=<a; b>

RT=<a; b>

th

Ps=P(RT<th)=?



15

ADAMExecution

cRT

1

2

i

RT=<a; b>

j

RT=<a; b>

RT=<a; b>

th

Ps=P(RT<th)=?U~(a,b)



15

ADAMExecution

0 < P(RT<th) < 1a b

P(RT<th) th-cRT

RT

b-a1

cRT

1

2

i

RT=<a; b>

j

RT=<a; b>

RT=<a; b>

th




15

ADAMExecution

0 < P(RT<th) < 1a b

P(RT<th) th-cRT

RT

b-a1

a b

th-cRTP(RT<th) = 1

RT

P(RT<th)

b-a1

cRT

1

2

i

RT=<a; b>

j

RT=<a; b>

RT=<a; b>

th




15

ADAMExecution

0 < P(RT<th) < 1a b

P(RT<th) th-cRT

RT

b-a1

a b

th-cRTP(RT<th) = 1

RT

P(RT<th)

b-a1

th-cRT

a b RTP(RT<th) = 0

P(RT<th)

b-a1

cRT

1

2

i

RT=<a; b>

j

RT=<a; b>

RT=<a; b>

th




16

ADAMExecution

9a

9b

i

RT=<0.5; 1.1>E=<4; 5>U=<1; 2>

RT=<0.6; 1.2>E=<4; 5>U=<1; 2>

j

RT=<0; 0.6>E=<2; 3>U=<1; 2>



16

ADAMExecution

9a

9b

i

RT=<0.5; 1.1>E=<4; 5>U=<1; 2>

RT=<0.6; 1.2>E=<4; 5>U=<1; 2>

j

RT=<0; 0.6>E=<2; 3>U=<1; 2>

cRT=1.95s



16

ADAMExecution

9a

9b

i

RT=<0.5; 1.1>E=<4; 5>U=<1; 2>

RT=<0.6; 1.2>E=<4; 5>U=<1; 2>

j

RT=<0; 0.6>E=<2; 3>U=<1; 2>

Ps

9a 0.67

9b 0.75

opt 0.13

cRT=1.95s



16

ADAMExecution

9a

9b

i

RT=<0.5; 1.1>E=<4; 5>U=<1; 2>

RT=<0.6; 1.2>E=<4; 5>U=<1; 2>

j

RT=<0; 0.6>E=<2; 3>U=<1; 2>

Ps

9a 0.67

9b 0.75

opt 0.13

cRT=1.95s

cRT=2s



16

ADAMExecution

9a

9b

i

RT=<0.5; 1.1>E=<4; 5>U=<1; 2>

RT=<0.6; 1.2>E=<4; 5>U=<1; 2>

j

RT=<0; 0.6>E=<2; 3>U=<1; 2>

Ps

9a 0.67

9b 0.75

opt 0.13

cRT=1.95s

Ps

9a 0.63

9b 0.57

opt 0.65

cRT=2s



Overhead



Overhead

17

4%



Overhead



Overhead

18

9%



Related Work



Related Work

19

Balz et al. Fleurey et al.Cheng et al.



20

Conclusions



20

ConclusionsWe conceived a method for adaptivity w.r.t. non-functional requirements



20


Formally verified and automated approach: based on high level models and implementations



20


Formally verified and automated approach: based on high level models and implementationsInterpreter that optimizes the system at run-time w.r.t. non-functional requirements



20


Formally verified and automated approach: based on high level models and implementationsInterpreter that optimizes the system at run-time w.r.t. non-functional requirements

Adaptivity obtained dynamically composing the application considering alternative behaviors and optionalities with limited overhead



Questions

21

Giordano Tamburrelli University of Lugano, Switzerland

[email protected]





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