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NON SENSITIVE RELEASABLE TO PUBLIC
Future for CAX
Prof Dr Erdal Cayirci
June 2016
v. 25 SEP 2015
NON SENSITIVE RELEASABLE TO PUBLIC
Agenda
- HLA
- MSaaS
- Modelling Hybrid Environments
NON SENSITIVE RELEASABLE TO PUBLIC
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
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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
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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
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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
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Models
Types of Models
iconic symbolic
physical virtual
high resolution low resolution
high fidelity low fidelity
predictive explatory
supervised unsupervised
deterministic stochastic
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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
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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
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Distributed vs Centralized
RTI
federate1
federate2
Distributed Centralized
Federate 1
Federate 4 Federate 5
Federate 3 Federate 2
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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
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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?
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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
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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.
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Cerebellum Function
Cayirci E., H. Karapinar and L. Ozcakir, “Cerebellum Function for MSaaS”, The Proceedings of the 27th European Modelling & Simulation Symposium, September 2015.
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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.
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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
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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
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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
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Hybrid Warfare
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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)
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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.
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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.
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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
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Questions