Multi-Objective Optimization inRule-Based Design Space

Exploration

Hani Abdeen, Dániel Varró, Houari Sahraoui, András Szabolcs Nagy, Csaba Debreceni, Ábel Hegedüs, Ákos Horváth

International Conference on Automated Software Engineering (ASE 2014) Västerås, Sweden, September 15 - 19, 2014

Design Space Exploration (DSE)

• Special state space exploration– Potentially infinite state space– cannot put upper bound on the number of model elements used in a design

candidate (elements are created and deleted during exploration).

Design Space Exploration

Design Alternative 1

Design Alternative 2

Design Alternative 3

Design Alternative 4

Goals

Global Constraints

Operations

Initial Design

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Rule-based Design Space Exploration

• Objectives : complex model metrics calculated by model queries• Cost calculations may depend on the seq. of transf. rules• Multiple objectives

Design Space Exploration

Seq. of Transf. Rules 1

Seq. of Transf. Rules 2

Seq. of Transf. Rules 3

Seq. of Transf. Rules 4

Model queries as Goals

Model queries as Constraints

Transf. rules as Operations

Initial Model as a graph

Modified model

Operation

Initial model

Solution model

Constraints violated

Goals satisfied

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Motivating example: Smart Building

• Reconfiguration of supervising cyber-physical systems (CPS) – Offices to rent with highly

configurable services – Services to deploy on both

embedded and virtual computational units

– Requests may change over time– Certain faulty devices may no

longer function

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Motivating example: Smart Building

Architecture5

Smart Building: configuration modelServices and Requests

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(a) Services

(b) Two examples on company requests

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Approach : Basic Conceptsdom

ain

Model

New/Modified requirements

candidate Models

Incorporate changes

Exploration rules

Search ideal candidate

Objectives

Constraints

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Approach : Basic Concepts

MnMM nrere ...10Rn1

Rn

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Approach : Basic Concepts

• Constraints– Graph patterns to search for with model queries– For smart buildings

• Constraints define valid or invalid configurations

Positive Positive Positive Negative

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Approach : Basic Concepts

Positive for well-formedness constraintsNegative for ill-formedness constraints

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Approach : Basic Concepts

• Objectives– Satisfying all constraints is a top-level

objective – Depend on the domain – Derived from

• Models (Model objectives) • Rule applications (Trajectory objectives)

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Approach : Basic Concepts

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Approach : Basic Concepts

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Approach : Algorithm• NSGA-II

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Approach : Algorithm

• Constraint-handling strategy– A solution s1 constrained-dominate a solution

s2, if:1. s1 is valid and s2 is not 2. both s1 and s2 are invalid, but s1 has a smaller

overall constraint violation 3. both s1 and s2 are valid and s1 dominates s2 with

the usual domination function wrt. the optimization objectives.

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Approach : Algorithm

• Constraint-handling strategy

Rank 1Rank 2

…Rank 3

…Rank N

Rank 4

Rank 1Rank 2

…Rank 3

…Rank N

Rank 4

1Same fulfillment

value2

Other optimizationobjectives

Constraint fulfillment

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Approach: Algorithm

• Genetic operators

One-point crossover Cut-and-splice crossover Permutation crossover

Add mutation Delete mutation Swap mutation

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Experimental Evaluation• Data

– Smart building (re)configuration

• Comparison with: 1. Random simulation (Random)2. Fixed priority local search (FPLS) strategy

• Requests – Increasing number of rooms (4, 6, 8, 12)

Problem size (rooms) Model size (graph elements)

4 130

6 200

8 230

12 330

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Experimental Evaluation

• Initial model– Requests with requirements, and application

and host types – No instances

• DSE process 1. create a sufficient number of application and

host instances 2. allocate application instances to host instances3. start and stop the application instances

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Experimental Evaluation

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Experimental Evaluation Results

Quality of NSGA solutions produced in 30 runs for different problem sizes, as measured by normalized constraints’ fulfillment, cost and computer server utilization

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Experimental Evaluation Results

• Higher constraint fulfillment with NSGA• Lower Cost with NSGA• Results statistically significant (two-tailed Wilcoxon

tests)

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

201 286 315 377 383 550

Norm. Constraint Fulfillment

Cost

Constraint Fulfillement vs. Cost

NSGA

FPLS

Random

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Experimental Evaluation Results

• Higher computer server utilization with NSGA• Results statistically significant (two-tailed Wilcoxon

tests)

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00.10.20.30.40.50.60.70.80.9

1

201 286 315 377 383 550

Computer Server Utilization

Cost

Computer Server Utilization vs. Cost

NSGA

FPLS

Random

ConclusionDomain Model Requirements Exploration Rules

New Requirements

M0

Graph Patterns

Constraints

Optimization Objectives

On Model

On Trajectory

client

Domainexperts

………

Pareto solutions

…

Fittest Models

…

…

NSGA-based evolution withConstraint-handling strategyNSGA-based evolution withConstraint-handling strategy

Initial population

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