Force Protection Call 4 A-0938-RT-GC EUSAS European Urban Simulation for Asymmetric Scenarios

© EADS 2010 – All rights reserved

Force Protection Call 4A-0938-RT-GC

EUSASEuropean Urban Simulation for Asymmetric Scenarios

Scalarm: Massively Self-Scalable Platform

for Data Farming

© EADS 2010 – All rights reserved 2

Agenda

1. Introduction to Data Farming in the EUSAS project

2. The problem of Data Farming scale

3. Overview of Scalarm

4. Architecture of Scalarm

5. Resource management

6. Scalarm applications

7. Conclusions


Goals of data farming in EUSAS

During training sessions soldiers perform a mission (scenario). However, if we look closer, the simulation process is more complicated than it seems.Each agent can be parameterized!

Agent 1:•readiness for aggression - 10,•anger - 3,•fear - 12,•….

Agent 2:•readiness for aggression - 10,•anger - 10,•fear - 2,•….

Agents can also be divided into groups!

1. group that loots:•group size - 30,•aggression of members -10,•readiness for aggression of members - 5

2. group that is prone to violence

•group size - 12,•aggression of members -10,•readiness for aggression of members - 25

Leader of group 1:•radius of influence - 10,•prestige - 30,•…

Moreover, groups may have leaders.As a result, many scenarios of mission are possible but soldiers are able to execute mission only a few times during the training session.

In EUSAS project data farming provides:

• Identification of dependences between input parameters and simulation result (described by measures of effectiveness).

• Comparison of behaviour models/soldiers strategies.

• Selection of input parameters for training sessions

Result of the mission cannot be predicted simply on the basis of the few serious game executions of the scenario.

Data farming allows analysis of missions that have several different parameter combinations (possibly billions).


Introduction to Data Farming in the EUSAS project

• Data Farming, in general, enables discovery of useful insights in studied phenomena by providing large amounts of data for analysis.

• In the context of EUSAS, Data Farming is utilized to study agents’ behaviour in various scenarios in order to verify different engagement strategies.

• EUSAS developed a novel system, called Scalarm, to facilitate conducting large Data Farming experiments with heterogeneous computational infrastructure.


The problem of Data Farming scale – simple scenario• Two groups:

1. Looters group

2. Group that is prone to violence

• Two informal leaders:

• soldiers do not know who they are

• Many input parameters:

• group sizes, leader prestige, readiness for aggression…

• Many monitored MoEs:

• escalation, anger, number of killed or injured agents…

1

2

• Presented application of data farming system:

• Identification of dependencies between input parameters and simulation results (described by Measures of Effectiveness).


The problem of Data Farming scale

• Simple simulated scenario can include: • 2 individual agents representing group leaders (each with 22

parameters)

• 2 groups of agents (each group with 24 parameters)

• => 92 different parameters for a single scenario• Let’s suppose we want to check only 2 values for each

parameter => 2^92 different simulations• Let’s suppose a single simulation runs only for 1 second on

average => 157,019,284,536,451,074,949 compute years

• We need to filter input parameter combinations even more and have a lot of computing power at the backend.


Scalarm goals

• Simulating complex phenomena with multiple input parameters by running various types of simulation applications, e.g. multi-agent, optimization, etc.

• Self-scalable platform adapting to particular problem size and different simulation types

• Exploratory approach for conducting Data Farming experiments

• Supporting online analysis of experiment partial results• Running on Cloud, Grid and private cluster infrastructures


Scalarm architecture

Small experiment

Very large experiment

Standard experimentLarge experiment


Resource management

ClientClient ClientClient

SMSM

SiMSiM

EMEM

SiMSiMSiMSiMSiMSiM

SiMSiM SiMSiMSiMSiMSiMSiM

Computational resources

Computational resources

Platform management

resources

Platform management

resourcesFree

resourcesFree

resourcesExperiment conductingExperiment conducting

More workload

More workload

EMEMSMSM

SiMSiM

SiMSiM

SiMSiM

Workload change: shorter simulations =>

increase of management

overhead

Workload change: shorter simulations =>

increase of management

overhead

SMSM

EMEM

SMSM SMSM

EMEM

- worker node EM – Experiment Manager SM – Storage Manager SiM – simulation manager


Scalarm applications - TODO


Comparison of behaviour models/soldiers strategies

• Crowd behaviour depends on many input parameters:

• Behaviour model/strategy that works well for one parameters’ set may work badly for another.

Example:

First implementation of soldiers modelescalation mean: 153.53

Soldiers model created with MASDA:escalation mean: 72.22

• Different strategies may be compared in 3 steps:

1. Multiple execution of mission by soldiers (using different strategies).

2. Behaviour cloning.

3. Executing data farming experiment for each cloned strategy.

Conclusion: MASDA helped to choose strategies that work well in different conditions.


Conclusions

1. To enhance soldiers’ training, a large number of analysis of soldiers’ behaviour in different scenarios is required, thus Data Farming is a crucial module of the project.

1. EUSAS Data Farming module (implemented as Scalarm) constitutes a complete virtual platform for executing interactive Data Farming experiments.

1. Scalarm enables analysts to generate and analyze large amount of data with computer simulation in order to gain useful insight into simulated scenarios.


Thank you for your attention !

Force Protection Call 4 A-0938-RT-GC EUSAS European Urban Simulation for Asymmetric Scenarios

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