future for cax · 2017. 3. 16. · future for cax prof dr erdal cayirci june 2016 v. 25 sep 2015 ....

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Future for CAX

Prof Dr Erdal Cayirci

June 2016

v. 25 SEP 2015


Agenda

- HLA

- MSaaS

- Modelling Hybrid Environments


LVC

Live Virtual Constructive

Purpose Individual, Collective,

Field Exercises, Mission

Rehearsal, Experiments

Individual, Collective(-),

CAX(-), Mission

Rehearsal, Experiments

Individual, Collective, CAX,

Mission Rehearsal,

Wargames, Experiments,

Plan Analysis

Real Time Real Tïme Real Time Real Time<Sim Time<Real

Time

Distributed

Exercises

Always distributed

simulation

distributed exercise =

distributed simulation

distributed exercise

distributed simulation

Number of Entities Hundreds Tens Highly aggregated: millions

High resolution: thousands

Terrain If not air, limited to tens

of kilometers

If not air, limited to tens

of kilometers

Highly aggregated: Globe

High resolution: Hundreds of

kilometers


Wargame vs CAX

Wargames Command Post Exercises

Focus A Game (the winning strategies/ tactics)

A collective training (training the TA)

Structure Players, Referees Training audience and exercise control

Execution Set of Rules Set of exercise/training/experimentation objectives

Aim / Termination

Win the game (zero sum games, etc.)

Achieve the objectives

Nature Discrete time, Realism of the results is paramount, back and forth

Continuous, Realism of the environment is paramount


Wargame vs Analysis/Experiment

Wargames Analysis

Focus A Game A study, a closer examination of a course of action or a plan

Structure Players, Referees Planners, Analysts

Execution Set of Rules Set of objectives/intended outcomes, constraints, restrains

Aim / Termination

Win the game Find the feasible and better configuration to achieve the objectives. Satisfy the constraints/restrains. Understand the relations between parameters.

Nature Discrete, Realism is paramount, back and forth

Static, Discrete time, realism of the results is critical, confidence is to be established, highest game speed possible


Wargame, CAX, Analysis/Experiment

Name and Size of the Audience

Name and Size of the Control

Length of Execution Phase

Drivers Realism

Wargames Players (G1-G9)

Referees, <Players

5-15 days Dynamics, Rules of the game

1. Model and Data 2. Environment

Command Post Exercises

Complete HQ EXCON >Training Audience

5-15 days Objectives, Procedures

1. Environment 2. Model and Data

Plan Analysis, Assessment, Evaluation

Planners (G5)

Analysts Several

Months Measures, Constraints, Restrains

Model and Data


Models

Types of Models

iconic symbolic

physical virtual

high resolution low resolution

high fidelity low fidelity

predictive explatory

supervised unsupervised

deterministic stochastic


Simulations

Types of Simulations

discrete continuous

static dynamic

non interactive interactive

real time adjustable simulation speed

physical virtual

deterministic stochastic

centralized distributed

discrete event agent based

disconnected symbiotic


Layered vs Monolothic

Application

Transport

Network

Data Link

Physical

IP

ICMP

IGMP ARP RARP

BOOTP

DHCP

IEEE 802.11 IEEE 802.3

Wireless F/O TP Coax

UDP

ftp http smtp pop

TCP

RTI

federate1

federate2

TCP/IP Protocol Suite OSI HLA


Distributed vs Centralized

RTI

federate1

federate2

Distributed Centralized

Federate 1

Federate 4 Federate 5

Federate 3 Federate 2


Other Topics

- Homogeneous vs hetergenous federates (modelling approaches), is homogeneity achievable?

- Attribute/entity based distribution vs service based distribution - Scalability - Achiaveable speeds - Time management - Data distribution - Federation building


Conclusion for next HLA

- Does HLA support all kinds of models and simulations? - Does HLA support all kinds of simulation based methodologies? - Is HLA layered as architecture? - Is HLA distributed? - Is it agile enough? - Does it support the reusability of architectural components? - Is it practical and pragmatic? - Is it scalable? - Does it fit to future developments such as MSaaS?


Defense Process

Service Oriented Modelling and Simulation

as a Service

hTEC

Defence Planning

Advance

Planning Standing Defense Plan,

Contingency Plan,

Generic Contingency

Plan

Crises Response

Planning

Capability Package

Management

Doctrine

Development (Experiments and

Integration)

Education Individual

Training

Collective

Training and

Exercises


Platform as a Service Layer

Simulation / Session Layer

Model / Service Layer

Modelling / Service Composition Layer

Secu

rity

User Interface Layer

Platform as a Service

Infrastructure as a Service

Physical Infrastructure

Model as a Service

Modelling as a Service

Simulation as a Service

Users

Software as a Service

a. hTEC Layers b. Cloud Service Models Including MSaaS

Havelsan Training and Experimentation

Cloud (hTEC) and MSaaS

Cayirci E., “ Modelling and Simulation as a Cloud Service: A Survey,” In Proceedings of the 2013 Winter Simulation Conference, edited by R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, Washington DC, December 2013.


Cerebellum Function

Cayirci E., H. Karapinar and L. Ozcakir, “Cerebellum Function for MSaaS”, The Proceedings of the 27th European Modelling & Simulation Symposium, September 2015.


Examples for hTEC Services

Service Composition Layer SimulationApplication SimulationEngin ServiceRegistr

y InterfaceManager

ApplicationContext

Session Layer ExerciseManagementServic

e TimeManagementServic

e SynchronizationServic SessionContext

Service Layer RelationalDBAccessServic

e FilesystemAccessServic

e ObjectDBAccessService ConfigurationFileAccessService ImageAccessService

VideoAccessService

SoundAccessService

TerrainService

WeatherService

OceanService

SpaceService

WeaponEffectsService

TacticalMovementService

EntityEngineService

FaultCasualtyService

RuleEngineService

Secu

rity

Ser

vice

Cayirci E., H. Karapinar and L. Ozcakir, “hTEC: A Layered Architecture for MSaaS”, I/ITSEC, December 2016.


hTEC over Armada

EXCON

User

ODE 1

Server

Farm - 1

Data Center

- 1

HAVELSAN

Server

Farm

HAVELSAN

Data Center

Server

Farm - 2

Data Center

- 2

HAVELSAN

Armada Container as a Service

EXCON

User

ODE n

OSE

Training

Audience

1

Training

Audience

n


SDN for hTEC

Service Composition Layer SimulationApplication SimulationEngine ServiceRegistry

InterfaceManager ApplicationContext

Session Layer ExerciseManagementService TimeManagementService

SynchronizationService SessionContext

Service Layer RelationalDBAccessService FilesystemAccessService

ObjectDBAccessService ConfigurationFileAccessService

ImageAccessService

VideoAccessService

SoundAccessService

TerrainService

WeatherService

OceanService

SpaceService

WeaponEffectsService

TacticalMovementService

EntityEngineService

FaultCasualtyService

RuleEngineService

Secu

rity

Ser

vice

SD

N

Co

mp

osi

tio

n

Ap

p

SDN

Se

ssio

n

Ap

p

Northbound Interface


SDN for hTEC

Armada

Server

Farm - 1

Data

Center - 1

HAVELSAN

Server Farm

Data Center

Server

Farm - 2

Data

Center - 2

Control

Layer Southbound Interface

OpenFlow

Northbound Interface

API

SDN Session

App

SDN Composition

App

Application Layer

Control Layer

Infrastructure Layer


Hybrid Warfare


Hybrid Warfare The threshold depends on four parameters: 1. the normalization of the current level of instability (i.e., the defendant is getting

used to the situation), 2. STRATCOM by the opponent so, 3. STRATCOM by the defendant sd and 4. the resilience p of the defendant in all DIME+LI domains p (diplomatic), p

(informational), p (military), p (economic), p (law enforcement), p (intelligence)

Please note that the weight of each DIME+LI domains in overall power p of the defendant may be different from each other. In these equations, so, sd and p are real numbers between 0 and 1 (i.e., so, sd , p and 0≤so≤1, 0≤sd≤1, 0≤p≤1)

p = p + p + p + p + p + p

where + + + + + =1

= ( so) –( p sd)


Hybrid Warfare

𝑣 = (1 − 𝑅𝑐𝑘𝛼)

𝑛

𝑘=1

𝑡𝑖𝑛 𝑚𝑖

𝑐=1

𝑑ℎ

𝑣𝑖 =𝑅𝜌

𝑡𝑣𝑖−1 + 1 −

𝑅𝜌

𝑡 𝑣

The normalization parameter depends on the history, the types of the opponent’s actions and their frequencies. is the memory parameter. The number of events n in the last period i that the normalization parameter is evaluated for, and the length ti of the time interval between the last normalization evaluation and current time give the frequency (n/t) of events. The other important parameters for calculating the normalization factor are ethnical and religious divisions d (i.e., the number of ethnical and religious groups) and how much these divisions discriminate or tolerate (or even to support the opponent) h each other.


Hybrid Warfare

.

𝑎𝑖 = (𝑎𝑜𝑙)𝑐𝑘11+(𝑎𝑑)𝑐𝑘

𝑛𝑙

𝑘=1

𝑡𝑖𝑛 𝑚𝑖

𝑐=1

𝑎𝑟 = 𝑎𝑜𝑛 𝑐𝑘1+(𝑎𝑑)𝑐𝑘

𝑛𝑛

𝑘=1

𝑛𝑡𝑖 𝑚𝑖

𝑐=1

=𝑝 𝑠𝑑 𝑎𝑖 − 1 − 𝑝 𝑠𝑜 𝑎𝑟𝑑ℎ

The following parameters affect the willingness: - STRATCOM by the opponent so, - STRATCOM by the defendant sd, - the power p of the defendant in all DIME+LI to clarify and communicate the facts, - the effectiveness of the comprehensive actions ad by the defendant, - hybrid aon and non-hybrid aol actions by the opponent The division d and discrimination h parameters already explained in the previous subsection. A part nl of the number of events n are non hybrid, and the other part nn are hybrid actions. Therefore, n=nl+nn.


Hybrid Warfare

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

1 5 10 15 20 25 30 35 40 45threshold

willingness

capacity

frequency

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

threshold

willingness

capacity

STRATCOM by defendant

-0.40

-0.20

0.00

0.20

0.40

0.60

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

threshold

willingness

capacity

STRATCOM by oponent

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

threshold

willingness

capacity

discrimination


Questions

future for cax · 2017. 3. 16. · future for cax prof dr erdal cayirci june 2016 v. 25 sep 2015 ....

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