The document discusses a high-performance interoperable architecture aimed at achieving information dominance, emphasizing the need for effective management of application performance, behavior, and data state. It outlines various interoperability levels and architectural approaches, contrasting application-centric, message-centric, and data-centric models, with a focus on a data-centric approach that delegates interoperability responsibilities to middleware. Additionally, it highlights challenges and best practices for integration, emphasizing the importance of decoupling application views from infrastructure management of state to enhance operational efficiency.

1. High-Performance Interoperable
Architecture for Information
Dominance
Gordon A. Hunt – RTI Webinar
Chief Engineer, RTI • UCS WG Sub-Committee Chair • Commander USN-R

2. Agenda
• High Performance
– It really is about time…
• Interoperable Architecture
– What is Interoperability
– Architecture foundations
• [for] Information Dominance
– Putting it all together
– Right time – right data – right place

3. So, a little about Performance.
Measure the right thing
for the right reason.

4. Performance
• What can we measure?
– Message rates
– Numbers of messages
– Size of messages
– Time to process messages
– Number of clients, number of servers
– Throughput of a broker/server
• Do the measurements matter?
– Consider that “Messages” are about something.
– What I really care about, is how long it takes to know
that something.

5. Performance is often Defined by…
Point-to-point, sockets, RPC, RMI
Concerned with an applications individual throughput/latency
Bottleneck at the slowest application
Little correlation between bus/fabric speeds and system performance
Centralized, DB, ESBs
Measure the aggregate performance
Broker Replication rates, transaction rates
ESB Number of supported clients
DBMS
Generally not measure client
performance
Decentralized, Data Centric
Measures data as presented to the application
Measures data update rates (1-1, 1-many, etc.)
Measures time to get the consistent state

6. Performance Does Matter
• Impacts Scale
– How big is the System-of Systems going to be?
– Physically can‟t send all the data everywhere.
– Can‟t revisit existing application when adding new capability.
• Impacts flexibility of Implementation
– Not arbitrarily constrained to messages rates/sizes.
– Capability and distribution can be added.
• However, the following is required
– Applications need to manage their performance requirements
– Applications need to manage their behavior explicitly

7. Application Behavior Concerns
• What data is valid
– Do I have the current value(s), from everyone
• How much of the data do I need
– Reliably, only get me date that changes by a little bit
– Send me data no faster than this rate
• What happens if
– I expected an update to data at …, what do you do.
– I want an acknowledgement, wait for this long, for this many
• I‟m too busy
– Old data that I am just getting around to looking at
– Are the updates I‟m about to send even valid anymore?

8. Managed Performance for
Information Dominance
Application Application
Quality of Service Quality of Service
(Desired Behaviors,..) (Desired Behaviors,..)
Integration Bus for Information Dominance
Operational Systems / Tactical Systems Information Technology (IT)
Command & Control (C2)
DATA-IN_MOTION DATA-AT-REST

9. An Integration Capability that
• Supports Integration of applications and
delivery of consistent behaviors when
– Environment is ADHOC
– Performance concerns are mixed
– Scale is unknown
– Technologies are mixed
• Enables Information Dominance via
– Architectural approach that supports a rigorous yet
flexible integration methodology that is interoperable
across data form/function boundaries

10. Interoperable Architecture
How Do We „Do‟ Interoperability?
What does the Architecture look like?

11. Current Approaches
• Protocol Definitions & Standards
– Tell me the messages
• Open Architecture Mandates
– Interoperability on Commonality
• Service Oriented Architecture
– Stateless services

12. Is Current Practice Working
• Recent studies have shown a growth in interoperability
policy issuance in DoD
– Thousands of pages of directives, instructions, and mandates
– Numerous standards and architecture bodies in the DoD
• No Correlation between Increased Interoperability and
Standards
– Standards are necessary, but not sufficient for interoperability
• Conventional means of developing platform, unit
command, and theater architectures are complex,
manpower intensive, and time consuming.
– Achieving Interoperability increases complexity
– Complexity of systems-of-systems not understood or well
managed
– Can‟t make complexity go away, just move where it is

13. Pause: What are we Trying to
Achieve?
Driver
Interchangeability To put each of (two things) in the place of the other, or to be used in place of
each other.
Integratability To form, coordinate, or blend into a functioning or unified whole. To incorporate
into a larger, functioning or unified whole.
Replaceability One thing or person taking the place of another especially as a substitute or
successor.
Extensibility The ability to add new components, subsystems, and capabilities to a system.
Interoperability The ability of systems, units, or forces to provide services to and accept services
from other systems, units, or forces, and to use the services so exchanged to
enable them to operate effectively together.
Open Architecture Requires Interoperability at a Higher Level
Than Key Interfaces.

14. Levels of Interoperability
-- Technical --
System Behavior Non-Functional Need
Communication protocol Interchangeability,
for exchanging Integrateability,
data. Bits & Bytes Replaceability,
are exchanged in Extensibility,
an unambiguous and Meaningful
manner. Technical Interoperability

15. Levels of Interoperability
-- Syntactic --
System Behavior Non-Functional Need
Common structure Interchangeability,
or data format Integrateability,
defined for Replaceability,
Syntactic
exchanging Extensibility,
information.
And Meaningful
This format
Technical Interoperability
must be
unambiguously
defined.

16. Levels of Interoperability
-- Semantic --
System Behavior Non-Functional Need
Meaning of data Semantic
Interchangeability,
is exchanged Integrateability,
via common Replaceability,
Syntactic
information Extensibility,
model. And Meaningful
Interoperability
The meaning Technical
of information
is shared and
unambiguously
defined.

17. What‟s the Difference?
• Semantic definition captures concepts of
– Structure (Content)
– Relationships (Context)
– Time (Behaviors)
• This makes the state of the system and the
application‟s data
– Explicit
– Directly Observable
– Manageable
• Everything has state…

18. Why is this hard?
Consider a measure of
complexity on application
development approaches.

19. How to Architect Systems to
Achieve Interoperability
1. Application Centric: Building Interoperable Apps
– Application manages Technical, Syntactic, and Semantic Interoperability
2. Message Centric: Building Interoperable Messaging Systems
– Delegates Syntactic Interoperability to Messaging Middleware
– Application manages Semantic Interoperability
3. Data Centric: Building Truly Data Centric Systems
– Delegates Semantic Interoperability to Middleware
– Open Architecture Requires a Data Centric Approach.
19

20. Application Centric Development
• Scales O(n) only if
– fully known, distributed, and homogeneous
knowledge of interfaces and state
Data State: Color denote uniqueness
Node/Service
Interface: Colors denote uniqueness
20

21. Application Centric Development
• Scales O(n2) only if
– each System invokes the Specified Interface of the
System that it wishes to communicate with and no
application state
– n is the number of interfaces
21

22. Application Centric Development
• Scales O(n3)
– Each system must understand the interaction
patterns and the remote data state….
– n is the number of data states
22

23. Application Centric Development
• Small systems aren‟t the problem, and
they give a false sense of hope.
– This is ~3.5 time more „complex‟ than 2 nodes
23

24. Application Centric Development
• O(n3) Scaling
– ~8 times more
complex
24

25. Message Centric Development
• Delegate syntactic
interoperability
– Scales O(n2)
– State is still in Apps
25

26. Data-Centric Development
• Delegate to middleware
Syntactic and Semantic
Interoperability
– Scales O(n)
– State synced with middleware
– State IN the middleware
and infrastructure
– Data-Centric Pub/Sub
26

27. An Interoperable Architecture
HAS a Data-Centric bus and is
Semantically Defined.

28. What is State?
• Things have attributes and
characteristics
– The meeting will run 1:00–2:00 in the
“State” (“data”) is a
conference room. snapshot of these
– My friend‟s phone number is 555-1234
and he‟s currently grooming his cat. attributes and
– The car is blue and is traveling north
from Sunnyvale at 65 mph.
characteristics.
– The UAS is performing this mission,
with these goals and resources.
Best Practice:
…whether they exist in the real operate on
world, in the computer, or both
state, not dialogs
…whether or not we observe or about state.
acknowledge them

29. Data-Centric is Explicit
Understanding of State
Weather ‘Client’ Weather Service
- Asks for a Weather Report - Stateless Service
- Get the current prediction - Provides current weather
only when asked
State
- The fact that I asked,
- Where, at a certain time
- ‘Somebody’ has to keep it current…

30. Example: Data-Centric FlightPlan
Start with a Semantic understanding of FlightPlans
Content is understood – can represent data efficiently
Context is understood, know what‟s what….
Dispose New Update New
Subscribe
Publish
“AA123” “DL987” “AA123” “AA123”
65.4 56.7 45.6
X 32.1 89.0 78.9

31. Example: Data-Centric FlightPlan
Quality of Service
FlightPlan
FlightPlan
FlightPlan
FlightPlan History
Deadline
Time-Based Filter
• Once infrastructure understands objects, can attach
behavior (QoS) contracts to them
• Data-in-motion behavior includes time perspective
Subscribe
Publish
• “Keep only the latest value” or “I need updates at
this rate” make no sense unless per-object
– Flight AA123 updates shouldn‟t overwrite DL987,
even if AA123 is updated more frequently
– Update rate for one track shouldn‟t change just because
another track appeared

32. [for] Information Dominance
Making it Happen, Steps you
can take…
32

33. Challenges for Information
Dominance
• Consistent Application Performance
– Regardless of Scale
– Numbers of nodes, amount of data, data rates…
• Dynamic systems‟ topology
– No system always on, nodes come and go
– Capabilities derived from rapid integration
• Disparate Technologies
– Different deployment concerns
– Different interaction patterns and behaviors
• Data-in-Motion and Data-at-Rest
– Making at all actionable at the right place & time

34. Data-Centric Infrastructure
Supports Information Dominance
Application Application
Quality of Service Quality of Service
(Time, Performance,..) (Time, Performance,..)
Integration Bus for Information Dominance
Operational Systems / Tactical Systems Information Technology (IT)
Command & Control (C2)
DATA-IN_MOTION DATA-AT-REST

35. RTI Connext :
Edge-to-Enterprise Real-Time SOA
Connext Integrator
Operational Systems Information
Technology (IT)
Connext Connext Connext
Micro DDS Messaging
Facilitates cross-organizational integration:
• Well-defined and discoverable information models;
• Standard data-centric operations
• Standard wire interoperability protocol
• Mediation: integrate with any interface, expose via any interface

36. RTI Connext™: A Data-Centric
Infrastructure
Discrete OT & IT
Small Device General-Purpose Apps/Systems
DDS Apps
Apps Real-Time Apps
Pub/Sub API Pub/Sub API Messaging API Adapters
Connext Connext Connext Connext
Micro DDS Messaging Integrator
RTI DataBus™
Administration Recording Federation
Monitoring Replay Transformation
Logging Visualization Persistence
Common Tools and Infrastructure Services

37. Summary
• Information Dominance is enabled by
– Access to the right information
– Managed expectations with regards to performance
• Access to the right information is facilitated by
– The right architecture and description of data
– Content, Context, and Behavior
• Leveraging the data is facilitated by
– Having that data presented to the application in the
right form and function
– Decoupling the applications view from the
infrastructures management of the state

38. Where is State
Point-to-point, sockets, RPC, RMI
State is in the applications
Each application maintains its view
Centralized, DB, ESBs
State is in the Database
Broker Managed interactions with state
ESB
DBMS
Decentralized, Data Centric
State is in the bus
Stateless clients/services
State has explicit properties to manage its behavior

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