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1 Future for CAX Prof Dr Erdal Cayirci June 2016 v. 25 SEP 2015

2 Agenda - HLA - MSaaS - Modelling Hybrid Environments

3 LVC Purpose Live Virtual Constructive 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

4 Wargame vs CAX Focus Wargames A Game (the winning strategies/ tactics) Command Post Exercises A collective training (training the TA) Structure Players, Referees Training audience and exercise control Execution Set of Rules Set of exercise/training/experimentati on objectives Aim / Termination Nature Win the game (zero sum games, etc.) Discrete time, Realism of the results is paramount, back and forth Achieve the objectives Continuous, Realism of the environment is paramount

5 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 Nature Win the game Discrete, Realism is paramount, back and forth Find the feasible and better configuration to achieve the objectives. Satisfy the constraints/restrains. Understand the relations between parameters. Static, Discrete time, realism of the results is critical, confidence is to be established, highest game speed possible

6 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

7 Models iconic physical high resolution high fidelity predictive supervised deterministic Types of Models symbolic virtual low resolution low fidelity explatory unsupervised stochastic

8 Simulations discrete static non interactive real time physical deterministic centralized discrete event disconnected Types of Simulations continuous dynamic interactive adjustable simulation speed virtual stochastic distributed agent based symbiotic

9 Layered vs Monolothic OSI Application Transport Network Data Link Physical TCP/IP Protocol Suite ftp http smtp pop TCP UDP ICMP DHCP IGMP IP BOOTP ARP RARP IEEE IEEE Wireless F/O TP Coax HLA federate1 RTI federate2

10 Distributed vs Centralized Distributed Centralized Federate 2 Federate 3 federate1 Federate 1 RTI Federate 4 Federate 5 federate2

11 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

12 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?

13 Defense Process Defence Planning Advance Planning Standing Defense Plan, Contingency Plan, Generic Contingency Plan Crises Response Planning Capability Package Management Service Oriented Modelling and Simulation as a Service htec Doctrine Development (Experiments and Integration) Education Individual Training Collective Training and Exercises

14 Security Havelsan Training and Experimentation Cloud (htec) and MSaaS User Interface Layer Users Simulation / Session Layer Simulation as a Service Modelling / Service Composition Layer Model / Service Layer Software as a Service Modelling as a Service Model as a Service Platform as a Service Layer Platform as a Service Infrastructure as a Service Physical Infrastructure a. htec Layers b. Cloud Service Models Including 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.

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

16 Security Service Examples for htec Services ExerciseManagementServic SynchronizationServic e SimulationApplication ApplicationContext Session Layer TimeManagementServic SessionContext e Service Composition Layer ServiceRegistr InterfaceManage y r Service Layer SimulationEngin RelationalDBAccessServic ObjectDBAccessService e FilesystemAccessServic ConfigurationFileAccessServic e ImageAccessService WeatherService SoundAccessService e TerrainService WeaponEffectsServic EntityEngineService e VideoAccessService OceanService RuleEngineService TacticalMovementServic e FaultCasualtyService SpaceService Cayirci E., H. Karapinar and L. Ozcakir, htec: A Layered Architecture for MSaaS, I/ITSEC, December 2016.

17 htec over Armada EXCON User ODE 1 Training Audience 1 OSE EXCON User ODE n Training Audience n Server Farm - 1 HAVELSAN Armada Container as a Service Server Farm - 2 Data Center - 1 HAVELSAN Server Farm Data Center - 2 HAVELSAN Data Center

18 Security Service SDN Composition App SDN Session App SDN for htec ExerciseManagementService Session Layer TimeManagementService SynchronizationService SessionContext SimulationApplication Service Composition Layer ServiceRegistry ApplicationContext InterfaceManager RelationalDBAccessService ObjectDBAccessService Service Layer SimulationEngine FilesystemAccessService ConfigurationFileAccessService ImageAccessService WeatherService SoundAccessService TerrainService VideoAccessService TacticalMovementService Northbound Interface WeaponEffectsService EntityEngineService OceanService FaultCasualtyService RuleEngineService SpaceService

19 SDN for htec Application Layer SDN Composition App Northbound Interface API SDN Session App Infrastructure Layer Control Layer Southbound Interface OpenFlow Control Layer Server Farm - 1 Server Farm - 2 Armada Data Center - 1 HAVELSAN Server Farm Data Center - 2 Data Center

20 Hybrid Warfare

21 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 s o, 3. STRATCOM by the defendant s d 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, s o, s d and p are real numbers between 0 and 1 (i.e., s o, s d, p and 0 s o 1, 0 s d 1, 0 p 1) p = p + p + p + p + p + p where =1 = ( s o ) ( p s d )

22 Hybrid Warfare 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 t i 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. d h mi v = (1 R ckα ) n t i n c=1 k=1 v i = R ρ t v i R ρ t v

23 Hybrid Warfare The following parameters affect the willingness: - STRATCOM by the opponent s o, - STRATCOM by the defendant s d, - the power p of the defendant in all DIME+LI to clarify and communicate the facts, - the effectiveness of the comprehensive actions a d by the defendant, - hybrid a on and non-hybrid a ol actions by the opponent The division d and discrimination h parameters already explained in the previous subsection. A part n l of the number of events n are non hybrid, and the other part n n are hybrid actions. Therefore, n=n l +n n.. a i = a r = mi c=1 m i c=1 n l k=1 n n k=1 (a ol ) ck 1 1+(ad ) ck a on ck 1+(a d ) ck t i n n ti = p s d a i 1 p d h s o a r

24 Hybrid Warfare threshold willingness capacity frequency threshold willingness capacity STRATCOM by defendant threshold willingness capacity STRATCOM by oponent threshold willingness capacity discrimination

25 Questions