Combining Genetic Algorithms & Simulation to

Search for Failure Scenarios in System Models

The 5th International Conference on Advances in System Simulation

Oct. 27-Nov. 1, 2013

Project Team:

Kevin Mills, computer scientist – kmills@nist.gov

Chris Dabrowski, computer scientist – cdabrowski@nist.gov

Jim Filliben, statistician – jfilliben@nist.gov

Sandy Ressler, information visualization specialist – sressler@nist.gov
Outline

• Method

• Cloud Computing Case Study in 7 Acts

1. Simulator

2. Parameters & Representation as Chromosomes

3. Genetic Algorithm

4. Population of Simulators

5. Dynamics of GA Search

6. Analysis Method

7. Results from Three GA Searches

• Conclusions

Oct. 27-Nov. 1, 2013 SIMUL 2013 2
Method: Genetic Algorithm (GA) steers a population of 

 simulators to search for parameter combinations 

 that lead to system failure

Model Parameter 

Specifications Parallel Execution of

Model Simulators

Population of Model

Parameterizations

Selection based on

Anti-Fitness

Recombination

& Mutation

List of parameters 

and for each

parameter a MIN, 

MAX and

precision.

Anti-Fitness Reports

GENETIC ALGORITHM

Principal Components Analysis,

Clustering, ...

MULTIDIMENSIONAL ANALYSIS TECHNIQUES

Growing Collection of Tuples: {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} ... {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value}

MODEL SIMULATORS

Oct. 27-Nov. 1, 2013 SIMUL 2013 3
Model Parameter 

Specifications Parallel Execution of

Model Simulators

Population of Model

Parameterizations

Selection based on

Anti-Fitness

Recombination

& Mutation

List of parameters 

and for each

parameter a MIN, 

MAX and

precision.

Anti-Fitness Reports

GENETIC ALGORITHM

Principal Components Analysis,

Clustering, ...

MULTIDIMENSIONAL ANALYSIS TECHNIQUES

Growing Collection of Tuples: {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} ... {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value}

MODEL SIMULATORS

4

Act 1: Koala Simulator

In our case study we defined anti-fitness as the proportion of arriving users not served

Oct. 27-Nov. 1, 2013 SIMUL 2013
 INTERNET

DEMAND LAYER

SUPPLY 

LAYER

VM 

BEHAVIOR

 LAYER

RESOURCE 

ALLOCATION

 LAYER

User 

#1

User 

#2

User 

#n

User 

#3

User 

#4

User 

#5

User 

#6

User 

#n-3

User 

#n-2

User 

#n-1

CLOUD CONTROLLER

Cluster 

Controller #1

Cluster 

Controller #c

NODE CONTROLLER #k

NODE RESOURCES NODE CONTROLLER #k-1

NODE RESOURCES NODE CONTROLLER #k-2

NODE RESOURCES

NODE CONTROLLER #3

NODE RESOURCES NODE CONTROLLER #2

NODE RESOURCES NODE CONTROLLER #1

NODE RESOURCES

NODE CONTROLLER #p

NODE RESOURCES NODE CONTROLLER #p-1

NODE RESOURCES NODE CONTROLLER #p-2

NODE RESOURCES

NODE CONTROLLER #3

NODE RESOURCES NODE CONTROLLER #2

NODE RESOURCES NODE CONTROLLER #1

NODE RESOURCES

NODE CONTROLLER #q

NODE RESOURCES NODE CONTROLLER #q-1

NODE RESOURCES NODE CONTROLLER #q-2

NODE RESOURCES

NODE CONTROLLER #3

NODE RESOURCES NODE CONTROLLER #2

NODE RESOURCES NODE CONTROLLER #1

NODE RESOURCES

INTRANETINTRANETINTRANET

VMs 

Shown for

One Node Only

VMs 

Shown for

One Node Only

VMs 

Shown for

One Node Only

VM1VM1VM1VM1 VM1VM1VM1VM1VM1 VM1VM1VM1VM1VM1 VM1

Cluster 

Controller #cm

(1)

(4)

(4) (3)

(2)

(4) (4)

(5)

Schematic of Koala IaaS Cloud Simulator

Demand and supply layers modeled after Amazon EC2 

Internal structure modeled after Eucalyptus v1.6

Oct. 27-Nov. 1, 2013 SIMUL 2013 5

Mention of specific commercial products does not imply endorsement by NIST
Model Parameter 

Specifications Parallel Execution of

Model Simulators

Population of Model

Parameterizations

Selection based on

Anti-Fitness

Recombination

& Mutation

List of parameters 

and for each

parameter a MIN, 

MAX and

precision.

Anti-Fitness Reports

GENETIC ALGORITHM

Principal Components Analysis,

Clustering, ...

MULTIDIMENSIONAL ANALYSIS TECHNIQUES

Growing Collection of Tuples: {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} ... {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value}

MODEL SIMULATORS

6

Act 2: Koala Parameters & GA Representation

 as Chromosomes (i.e., bit strings) 

Oct. 27-Nov. 1, 2013 SIMUL 2013
Model

Element

Parameter Category

Behavior Structure Asymmetry Failure Total

User 28 2 4 0 34

Cloud Controller 21 4 5 0 30

Cluster Controllers 11 5 3 0 19

Nodes 6 0 0 14 20

Intra-Net/Inter-Net 4 11 2 9 26

Totals 70 22 14 23 129

Summary of Koala Parameters to Search Over

Average # values per parameter is about 6, so search space is ≈ 6129

i.e., ≈ 10100 scenarios are possible

 adapted 125-parameter Koala IaaS simulator to be GA controllable 

 added 4 Koala parameters to turn on/off logic to control (a) creation orphans, 

 (b) termination orphans, (c) relocation orphans and (d) administrator actions

Test Case – Can GA find VM Leakage due to message loss and lack of orphan control? 

Failure scenario found manually by accident and described in C. Dabrowski and K. Mills, "VM Leakage and Orphan Control in 

Open-Source Clouds", Proceedings of IEEE CloudCom 2011, Nov. 29-Dec. 1, Athens, Greece, pp. 554-559.

Oct. 27-Nov. 1, 2013 SIMUL 2013 7
Sample Chromosome Specification

PARAMETER MIN MAX PRECISION #VALUES LOW_BIT HIGH_BIT #BITS

P_CreateOrphanControlOn 0 1 1 2 3 6 3 6 1

P_TerminationOrphanControlOn 0 1 1 2 5 8 5 8 1

P_RelocationOrphanControlOn 0 1 1 2 1 1 1 1 1

P_AdministratorActive 0 1 1 2 330 330 1

P_clusterAllocationAlgorithm 0 5 1 6 3 1 3 3 3

P_describeResourcesInterval 600 3600 600 6 8 1 8 3 3

P_nodeResponseTimeout 3 0 9 0 3 0 3 210 211 2

P_TerminatedInstancesBackOffThreshold 3 6 1 4 5 6 5 7 2

P_TerminationBackOffInterval 180 360 6 0 4 8 8 8 9 2

P_TerminationRetryPeriod 600 1200 300 3 316 317 2

P_StaleShadowAllocationPurgeInterval 600 3600 600 6 242 244 3

P_cloudAllocationCriteria 0 3 1 4 321 322 2

P_clusterShadowPurgeLimit 1 2 1 5 5 290 292 3

P_instancePurgeDelay 180 600 6 0 8 9 8 100 3

P_clusterEvaluationResponseTimeout 6 0 120 3 0 3 1 4 1 5 2

P_MaxPendingRequests 1 1 0 1 1 0 7 2 7 5 4

P_CloudTerminatedInstancesBackOffThreshold 3 6 1 4 169 170 2

P_CloudTerminationBackOffInterval 180 360 6 0 4 4 0 4 1 2

P_CloudTerminationRetryPeriod 3600 10800 1800 5 297 299 3

P_ClusterShutdownGracePeriod 86400 2.59E+05 43200 5 147 149 3

P_RequestEvaluatorTimeoutWaitProportion 0.1 0.4 0.1 4 145 146 2

P_RequestEvaluatorClusterMinimumResponse 0.6 0.9 0.1 3 269 270 2

P_MaxRelocationDuratonProportion 0.65 0.95 0.1 4 9 0 9 1 2

P_MaximumRelocateDescribeRetries 4 1 6 2 7 254 256 3

P_AverageCloudAdministratorAttentionLatency 28800 86400 14400 5 308 310 3

P_AverageCloudAdministratorShutdownDelay 300 900 300 3 4 5 4 6 2

P_avgTimeToClusterCommunicationCut 2.88E+06 2.88E+07 2.88E+06 1 0 217 220 4

Genetic Algorithm Computed

Chromosome Map (Size = 2334)

Koala Parameter 

Space (Size = 10100)

Oct. 27-Nov. 1, 2013 SIMUL 2013 8
Model Parameter 

Specifications Parallel Execution of

Model Simulators

Population of Model

Parameterizations

Selection based on

Anti-Fitness

Recombination

& Mutation

List of parameters 

and for each

parameter a MIN, 

MAX and

precision.

Anti-Fitness Reports

GENETIC ALGORITHM

Principal Components Analysis,

Clustering, ...

MULTIDIMENSIONAL ANALYSIS TECHNIQUES

Growing Collection of Tuples: {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} ... {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value}

MODEL SIMULATORS

9

Act 3: Genetic Algorithm

Oct. 27-Nov. 1, 2013 SIMUL 2013
Genetic Algorithm Flow Chart

Generate Needed Random Population of Individuals

Terminate? Evaluate Fitness Stop

Elite Individuals? Population of Individuals

YES

YES COPY TO NEXT GENERATION Select Candidate Individuals NO or YES

Reboot? Elite Individuals?

YES COPY TO NEXT GENERATION

NO or YES YES NO

Candidate Individuals

Choose Pair From Candidate Individuals Crossover? Choose Crossover Points & Swap Chromosomes Population of Recombined Individuals

Mutate Individual? Iterate Over Individuals Flip Chromosomes as Required

Start

YES NO

YES NO

INSERT PAIR INTO RECOMBINED POPULATION

INSERT INSERT IN NEXT POPULATION

INSERT PAIR

NO

SELECT

RECOMBINE

MUTATE

REBOOT?

EVALUATE

SEED

POPULATION

DONE?

1

2

3

4

5

6

7

Oct. 27-Nov. 1, 2013 SIMUL 2013 10
Model Parameter 

Specifications Parallel Execution of

Model Simulators

Population of Model

Parameterizations

Selection based on

Anti-Fitness

Recombination

& Mutation

List of parameters 

and for each

parameter a MIN, 

MAX and

precision.

Anti-Fitness Reports

GENETIC ALGORITHM

Principal Components Analysis,

Clustering, ...

MULTIDIMENSIONAL ANALYSIS TECHNIQUES

Growing Collection of Tuples: {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} ... {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value}

MODEL SIMULATORS

11

Act 4: Population of Koala Simulators

Oct. 27-Nov. 1, 2013 SIMUL 2013
12

Head Node 1

Head Node 2

Shared File

Store

Compute Node 1 Compute Node 2 Compute Node 12

Compute Node 13 Compute Node 14 Compute Node 24

Enterprise Network

Cluster Network

License Server 8 Koala Processes

8 Koala Processes

12 Koala Processes

12 Koala Processes 12 Koala Processes

200 Koala Working Directories GA Checkpoint Database

Node Manager Job Manager

GA Controller GA Management Processes

CLUSTER GA Results Tuples

Population of Koala Simulators Deployed on a High 

Performance Computing Cluster

Oct. 27-Nov. 1, 2013 SIMUL 2013
Model Parameter 

Specifications Parallel Execution of

Model Simulators

Population of Model

Parameterizations

Selection based on

Anti-Fitness

Recombination

& Mutation

List of parameters 

and for each

parameter a MIN, 

MAX and

precision.

Anti-Fitness Reports

GENETIC ALGORITHM

Principal Components Analysis,

Clustering, ...

MULTIDIMENSIONAL ANALYSIS TECHNIQUES

Growing Collection of Tuples: {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} ... {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value}

MODEL SIMULATORS

13

Act 5: Dynamics of GA Search

Oct. 27-Nov. 1, 2013 SIMUL 2013
14

Generations 500

Population Size 200 Individuals

Elite Per Generation 16 Individuals

Reboot After 200 Generations

Selection Method Stochastic Uniform Sampling

# Crossover Points 3

Mutation Rate 0.001 < Adaptive < 0.01

GENETIC ALGORITHM CONTROL PARAMETERS

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 100 200 300 400 500

Generation

(a) Average Anti-Fitness

0.7

0.72

0.74

0.76

0.78

0.8

0.82

0.84

0 100 200 300 400 500

Generation

(c) Maximum Anti-Fitness Discovered

One GA Search over 500 Generations

0

0.05

0.1

0.15

0.2

5

101520253035404550556065707580859095

100

Anti-Fitness

(b)

Frequency Distribution of Anti-Fitness

105 scenarios, i.e., 200 individuals x 500 generations 

84% of scenarios exhibit anti-fitness ≥ 0.50

Only 8% of scenarios 

are duplicate (equals 

elite-selection 

percentage) For Koala simulator, failure scenarios appear 

within first 100-200 generations

Average anti-fitness oscillates round 65% of users 

not served

Oct. 27-Nov. 1, 2013 SIMUL 2013
Model Parameter 

Specifications Parallel Execution of

Model Simulators

Population of Model

Parameterizations

Selection based on

Anti-Fitness

Recombination

& Mutation

List of parameters 

and for each

parameter a MIN, 

MAX and

precision.

Anti-Fitness Reports

GENETIC ALGORITHM

Principal Components Analysis,

Clustering, ...

MULTIDIMENSIONAL ANALYSIS TECHNIQUES

Growing Collection of Tuples: {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} ... {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value}

MODEL SIMULATORS

15

Act 6: Analysis Method

Oct. 27-Nov. 1, 2013 SIMUL 2013
-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0 100 200 300 400 500 600 700

i = 684 Parameter-Value Pairs

P(PVi|f>0.70) - P(PVi|f<0.15) 

D

PVi 

Outliers contributing to failure scenarios

Outliers contributing to success scenarios

PVs exerting little influence on success or failure

16

Differential Probability Analysis

Oct. 27-Nov. 1, 2013 SIMUL 2013

PV pairs sorted

by decreasing D
Model Parameter 

Specifications Parallel Execution of

Model Simulators

Population of Model

Parameterizations

Selection based on

Anti-Fitness

Recombination

& Mutation

List of parameters 

and for each

parameter a MIN, 

MAX and

precision.

Anti-Fitness Reports

GENETIC ALGORITHM

Principal Components Analysis,

Clustering, ...

MULTIDIMENSIONAL ANALYSIS TECHNIQUES

Growing Collection of Tuples: {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value} ... {Generation, Individual, Fitness, Parameter 1 value,....Parameter N value}

MODEL SIMULATORS

17

Act 7: Results from Four GA Searches

Oct. 27-Nov. 1, 2013 SIMUL 2013
18

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0 100 200 300 400 500 600 700

CreateOrphanControlOn = FALSE

AvgUserRequestTimeout = 30

AvgThinkTime = 900

CreateOrphanControlOn = TRUE

NodesPerCluster = 200

UserRestPeriodMultiplier = 32

NodesPerCluster = 400

Analysis of Results from GA Search 1 – 500 Generations

Seeking Known Failure Scenario – search duration 30 days

D (y-axis) for 684 PV pairs (x-axis) for first GA search–outlier PV pairs labeled.

Oct. 27-Nov. 1, 2013 SIMUL 2013
19

Analysis of Results from GA Search 2 – 205 Generations

Seeking Previously Unknown Failure Scenarios – search duration 14 days

D (y-axis) for 677 PV pairs (x-axis) for first GA search–outlier PV pairs labeled.

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0 100 200 300 400 500 600 700

AvgUserRequestTimeout = 30

AvgThinkTime = 900

UserRestPeriodMultiplier = 32

NumberofSitesReservedForCloud = 2

NodesPerCluster = 200

MilesPerRouterHop = 500

MaxPerHopDelay = 0.9

StdUserRequestTimeout = 30

NodesPerCluster = 400

AvgUserRequestTimeout = 120

NumberofSitesReservedForCloud = 1

AvgThinkTime = 1500

MilesPerRouterHop = 1500

Oct. 27-Nov. 1, 2013 SIMUL 2013
-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0 100 200 300 400 500 600 700

NodesPerCluster = 600 UserTypeArraySelector = 4

AvgThinkTime = 1200 AvgUserRequestTimeout = 90

SiteDistanceMultiplier = 7

MinSiteCoordinate = 0 UserRestPeriodMultiplier = 32

StdUserRequestTimeout = 30

MinReservationRetries = 2

AvgInterUserStartDelay = 30

MinIntrasiteDelay = 2x10-8

MinClusters = 9

MaxSiteCoordinate = 16000

MaxPerHopDelay = 0.9

MaxIntrasiteDelay = 1.5x10-5

CloudSizeChangeFraction = 0.3

ClusterResponseTimeout = 120

MaxIntersiteLossRate = 10-7

PlatformTypeArraySelector = 4 ProbabilityImageNotFound = 10-6

ChoosePlatformTypesRandomly = FALSE

RandomizeMinMaxVMsRequested = TRUE

MinRestPeriods = 2

NodeResponseTimeout = 30

20

Analysis of Results from GA Search 3 – 209 Generations

Nudging up Some Parameter Ranges and Seeking Additional Failure Scenarios -

search duration 16 days

D (y-axis) for 680 PV pairs (x-axis) for first GA search–outlier PV pairs labeled.

Oct. 27-Nov. 1, 2013 SIMUL 2013
SUMMARY:

• Defined a design-time method, combing GA search with simulation, 

to seek failure scenarios in system models

• Applied the method in a case study, seeking (and finding) a known 

failure scenario in an existing cloud simulator

• Iterated search to reveal previously unknown failure scenarios

FINDINGS:

• GA searches explored predominantly non-duplicative scenarios with 

high anti-fitness

• Uncovered evidence that GA search can reveal insights about 

optimal parameters settings, while simultaneously searching for 

failure scenarios

• GA search should be pursued only for systems with sufficient 

schedule time, and where failure scenarios have high cost

21

Conclusions

Oct. 27-Nov. 1, 2013 SIMUL 2013