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Mark Swick – RTI Webinar
Principal Applications Engineer, RTI
Two Approaches You Must Consider
when Architecting Radar Systems
© 2015 RTI
Agenda
• Background
– Radar (sensor) systems
• Architecture Commonality
– Generic system components
• Data Centric Design Patterns
– DDS patterns uniquely suited to data
requirements
• Data Commonality
– Making sense of it all
• Summary
© 2015 RTI
Background
What is a Radar?
© 2015 RTI
Defintion
• radar (rāˈdär)
(an acronym derived from the phrase RAdio
Detection And Ranging)
1. a device or system for determining the
presence and location of an object by
measuring the direction and timing of radio
waves.
© 2015 RTI
© 2015 Real-Time Innovations, Inc.5
Air Search Radar
RADAR
© 2015 Real-Time Innovations, Inc.6
Air Search Ground Radar
RADAR
© 2015 Real-Time Innovations, Inc.7
Ships and Radars
RADAR
© 2015 Real-Time Innovations, Inc.8
Aircraft and Radar
RADAR
© 2015 Real-Time Innovations, Inc.9
Ground Portable Radar
RADAR
© 2015 Real-Time Innovations, Inc.10
Long Lived Radar
RADAR
© 2015 Real-Time Innovations, Inc.11
Dish /= Radar
RADAR
NOT
RADAR
© 2015 Real-Time Innovations, Inc.12
Passive Listening
NOT
RADARRADAR
© 2015 Real-Time Innovations, Inc.13
Radar inside
RADAR
Software Architecture
What do these systems have in
common?
© 2015 RTI
Commonality in Architecture
© 2013 RTI
Receiving
Subsystem
Transmitting
Subsystem
User
Subsystem
Signal
Processing
Subsystem
Command
and Control
Subsystem Time and
Position
Subsystem
Storage
Subsystem
Tracking
Subsystem
Front End
(Harder Real-Time)
Back End
(Softer Real-Time)
Point to Point Communications
Architecture
© 2013 RTI
Receiving
Subsystem
Transmitting
Subsystem
Tracking
Subsystem
Command
and Control
Subsystem
User
Subsystem
Signal
Processing
Subsystem
Time and
Position
Subsystem
Cooperating
System
Storage
Subsystem
?
Data Centric Communications
Architecture
© 2013 RTI
Receiving
Subsystem
Transmitting
Subsystem
Tracking
Subsystem
Command
and Control
Subsystem
User
Subsystem
Signal
Processing
Subsystem
Time and
Position
Subsystem
Cooperating
System
Storage
Subsystem
TIME
SENSOR
DATA
TRACKS
STATE
POSITION
COMMAND
DISPLAY
DDS DDS
Non-real-time Soft real-time Hard real-time Extreme real-time
Java/RMIJava/JMS
CORBA
MPI
Java RTSJ (soft RT) RTSJ (hard RT)
Web Services
MessagingTechnologiesandStandards
Data Distribution Service / DDS
RT CORBA
Adapted from NSWC-DD OA Documentation
RTI Data Distribution Service spans a
very wide spectrum of application needs
© 2015 RTI
Open Systems
• Open Systems
– Are defined sufficiently that
so that multiple
organizations can work
cooperatively on the same
or separate sub-
components
– Have requirements which
are stable over
a sufficient length of time to
allow for concurrent
development
– Are documented fully and
openly to the development
community
– Are not under the control of
any one firm or vendor.
© 2015 RTI
© 2015 Real-Time Innovations, Inc.20
Key Non-Functional Requirements for a System
• Interchangeability
(Portability)
• Replaceability
• Extensibility
• Integratability
System
System
A
System
B
System
System
B
System
C
F(A,B)
Results in
X
F(C,B)
Results in
X
A and C
provide
Equal
Capability
© 2015 Real-Time Innovations, Inc.21
Key Non-Functional Requirements for a
System
• Interchangeability
• Replaceability
• Extensibility
• Integratability
System
System
A
System
B
System
System
B
System
C
F(A,B)
Results in
X, Y, Z
F(C,B)
Results in
Y, Z, W
C is NOT an
Equal
Capability, but it
Is a suitable substitute
© 2015 Real-Time Innovations, Inc.22
System
Key Non-Functional Requirements for a
System
• Interchangeability
• Replaceability
• Extensibility
• Integratability
System
System
B
System
C
F(A,B)
Results in
X
F(A,B,C)
Results in
X and Y
System
A
System
System
B
System
A
System
C
F(C)
Results in
Y
© 2015 RTI
© 2015 Real-Time Innovations, Inc.23
System C
Key Non-Functional Requirements for a
System
• Interchangeability
• Replaceability
• Extensibility
• Integratability
System
B
F(A)
Results
In X
F(A,B)
Results in
Z, where
Z=G[f(X), g(Y)]
System
A
System
B
System
A
F(B)
Results
in Y
© 2015 RTI
Infiniband
Subsystem B
Data Centric Integration Solution
1/30/2015 24
• Technical
Interoperability
– Infrastructure & Protocol
• Syntactic
Interoperability
– Common Data Structure
• Semantic
Interoperability
– Common Data Definition
TRACKS
STATE
COMMAND
DISPLAY
DDS DDS
Shared Memory
Subsystem A
TIME
SENSORDATA
STATE
POSITION
COMMAND
DISPLAY
DDS DDS
IPv6
Subsystem C
TIME
SENSOR
DAAA
TRACKS
STATE
POSITION
COMMAND
DISPLAY
DDS DDS
TIME
STATE
POSITION
DISPLAY
DDS DDS
Mediation Mediation
Mediation
IPv4
© 2015 RTI
Core Architecture
Built on Standard and Open Interfaces
RTI Connext DDS
Professional, Micro or Cert
Operating System (Linux, LynxOS, Windows, VxWorks, QNX, ….)
Optional FACE Transport Services API to DDS Mapping
25
Intra-
process
Shared
memory
ARINC
Ports
Sockets
Other/
Custom
RTITSSLibrary
FACE Transport
Services (TS) API
RTI transport
API
OMG DDS API
DDS-RTPS
protocol
Pluggable
transports
© 2015 RTI
Optimized, Location-Independent
Communication
• Physical transport(s) configurable at integration
time
• Applications can use multiple transports
concurrently
• Transport(s) configured per application
26
Transport Use
Intra-process Within the same address space (process)
Shared memory Between processes in the same partition
ARINC ports Within a node; within or between partitions
Sockets
(UDP unicast or multicast)
Within or between nodes, including over
Ethernet
Low-bandwidth Over satellite or radio links (no IP requirement)
Custom Over custom networks or busses (via plug-in API)
© 2015 RTI
Connection Mechanism
Comparison
27
RTIDDS
CORBA
Sockets
POSIX
Queues
Shared
memory
Queuing
ports
Sampling
ports
Proximity Intra-partition ● ● ● ● ● ● ●
Inter-partition ● ● ● ● ●
Inter-node ● ● ●
Multiple concurrently ●
Distribution One-to-one ● ● ● ● ● ● ●
One-to-many ● ● ● ● ●
Many-to-one ● ● ●
Many-to-many ● ●
● Unreliable
© 2015 RTI
DDS Design Patterns
How do they apply?
© 2015 RTI
Data Centric Interoperability
TIME
SENSOR
DATA
TRACKS
STATE
POSITION
COMMAND
DISPLAY
DDSDDS
Time/Position
Subsystem
External
Subsystem
Track
Subsystem
Mediation
Mediation
© 2015 RTI
Mediation : RTI Routing Service
30
Custom-DDS Translate – Routing Service
Custom
Plugin-In DW
Mode:
ON_DOMAIN
Mode:
ON_ROUTE
<<input>> <<output>>
<<participant_1>> <<participant_2>>
Route
Data Flow
DDS-DDS Translate – Routing Service
DR DW
Mode:
ON_DOMAIN
Mode:
ON_ROUTE
<<input>> <<output>>
<<participant_1>> <<participant_2>>
Route
Data Flow
DDS/RTPS Source DataDDS/RTPS Source Data
© 2015 RTI
© 2015 Real-Time Innovations, Inc.31
Data Distribution Design Patterns
TIME
SENSOR
DATA
TRACKS
STATE
POSITION
COMMAND
DISPLAY
DDSDDS
Objective/
State
One-to-
Many
High
Throughput
© 2015 Real-Time Innovations, Inc.32
Objective/State Design Pattern
3 States:
1. Current
2. Objective
3. Requested Objective
2+ Roles (special case of Observer pattern):
1. Effector
• Provides Current State and Objective State
• Observes Requested Objective State
2. Requester
• Provides Requested Objective State
• Observes Current State and Objective State
3. (Observer—with respect to any state)
Current
State
Objective
State
change
Effector executes this
Requested
Objective
State
Requester
changes
this
32
Objective/State with DDS
© 2012 RTI • COMPANY CONFIDENTIAL
RequesterEffector
 Durability QoS policy:
– Current, Objective: Transient_Local
– (Requested) Objective: Volatile
 History QoS policy:
– Current, Objective: Keep Last n
– Requested Objective: Keep Last 1
Data Bus
if long-running
if short-running
Current
State
write
Objective
State
write
Requested
Objective
State
read,
filter
request processed?
feedback
Current
State
read,
filter
Objective
State
read,
filter
same typesame or different types same key
Requested
Objective
State
write
© 2015 Real-Time Innovations, Inc.34
One-To-Many Design Pattern
 Observation: More common in naturally
data-centric interactions
– “Hey, look at this” vs.
– “Hey you: do this”
Consumer
Consumer
Consumer
…
Producer
1
2
n
© 2015 Real-Time Innovations, Inc.35
One-To-Many : Multicast Benefits
• Communicate to many consumers at the
same time much more cheaply than one-to-
one to each of them
– Less network traffic
– Lower latency (fewer socket sends)
– Lower writer-side CPU
• Can be reliable or “best effort”
– Configurable using DDS Quality of Service
© 2015 Real-Time Innovations, Inc.36
One-To-Many : Multicast Challenges
One-to-many reliability isn’t free
Actual RTI multicast results
Number of Readers
200-ByteSamplesperSecond
Why?
1. Slow consumers throttle writer
2. Reliability bandwidth overhead
Unicast expectation
Perfect multicast expectation
High Throughput Design Pattern
• Do samples arrive continuously or at a
high periodic rate?
• Is the transport saturated?
High Throughput Periodic Data
• RTI Connext…
– Sends synchronously by default
– Supports batching for high periodic rates
– Supports multiple reliability paradigms
– Supports receive processing in receive thread
or application thread
High Throughput over
Constrained Network
• RTI Connext…
– Supports configurable MTU sizes
– Supports batching in a manner with reduces
protocol header overhead
– Supports a Low-Bandwidth network plugin
with header and data compression
– Supports a “multi-channel” feature to send
data over different NICs as a function of data
content
Reliable High Throughput
• Lots to consider
– Writer must keep data for potential retransmission
– Latency unpredictability
– Readers must behave
– Design for desired behavior if data lost…
• Declare failure and stop
• Report error and keep going
• Delay writing for readers to catch up
• Do nothing
• …
The Data Matters
How do you Define & Design it?
© 2015 RTI
MODEL
A model is anything used in any way to represent something
else
42© 2015 RTI
DATA MODEL
A data model is a representation that describes the data about
the things that exist in your domain
43© 2015 RTI
Model and Implementation
• Model provides the Context and Semantics
– Containment and relationships
– May not necessarily be in the messages
• Messages can be compact
– Use the model for context
– ‘Know’ the association between a command and a
status
• Using machine readable context
– Can generate the system appropriate mediation
– Really only need the ID of ‘what’ in the message
I
© 2015 RTI
DDS Natively Supports
Interoperable Data Models
• DDS messages are strongly typed
• OMG IDL basis for native DDS Data Model schema
– XML, XSD, also supported
– Apps use target code generated by RTI’s IDL compiler
• DDS natively understands data
– Type safety
– Heterogeneous interoperability (languages, CPUs)
– Wire efficiency (minimizes metadata)
– Enables middleware-level filtering (including at source)
– Eases integration (explicit interfaces)
45
Platform Data
Model
RTI IDL
Compiler
C
C++
Java
Ada
Include in
application
source
© 2015 RTI
Summary
What were those two things, anyway?
© 2015 RTI
Create an Architecture
Consistent with Life Cycle
• Radar systems are often extremely long-lived
– Much longer than consumer product life-cycle
– Actively design for change with Data Centric
architecture
• Anticipate Multiple Technical Refresh cycles
– Open architectures and standards are key to
cost containment
– Know your data
I
© 2015 RTI
Focus on Domain Expertise
• Mechanical Design
• Algorithm Design
• Custom Hardware Design
• Compute plant and communications are
areas of constant change
– Communication Middleware isolates
system-specific software from processor
and network changes
– Changes inevitable over system life-cycle
I
© 2015 RTI
Start using DDS Today!
Download the FREE complete RTI Connext
DDS Pro package for Windows and Linux:
• Leading implementation of DDS
• C, C++, C#/.NET and Java APIs
• Tools to monitor, debug, test, visualize and
prototype distributed applications and systems
• Adapters to integrate with existing applications and
IT systems

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Two Approaches You Must Consider when Architecting Radar Systems

  • 1. Mark Swick – RTI Webinar Principal Applications Engineer, RTI Two Approaches You Must Consider when Architecting Radar Systems © 2015 RTI
  • 2. Agenda • Background – Radar (sensor) systems • Architecture Commonality – Generic system components • Data Centric Design Patterns – DDS patterns uniquely suited to data requirements • Data Commonality – Making sense of it all • Summary © 2015 RTI
  • 3. Background What is a Radar? © 2015 RTI
  • 4. Defintion • radar (rāˈdär) (an acronym derived from the phrase RAdio Detection And Ranging) 1. a device or system for determining the presence and location of an object by measuring the direction and timing of radio waves. © 2015 RTI
  • 5. © 2015 Real-Time Innovations, Inc.5 Air Search Radar RADAR
  • 6. © 2015 Real-Time Innovations, Inc.6 Air Search Ground Radar RADAR
  • 7. © 2015 Real-Time Innovations, Inc.7 Ships and Radars RADAR
  • 8. © 2015 Real-Time Innovations, Inc.8 Aircraft and Radar RADAR
  • 9. © 2015 Real-Time Innovations, Inc.9 Ground Portable Radar RADAR
  • 10. © 2015 Real-Time Innovations, Inc.10 Long Lived Radar RADAR
  • 11. © 2015 Real-Time Innovations, Inc.11 Dish /= Radar RADAR NOT RADAR
  • 12. © 2015 Real-Time Innovations, Inc.12 Passive Listening NOT RADARRADAR
  • 13. © 2015 Real-Time Innovations, Inc.13 Radar inside RADAR
  • 14. Software Architecture What do these systems have in common? © 2015 RTI
  • 15. Commonality in Architecture © 2013 RTI Receiving Subsystem Transmitting Subsystem User Subsystem Signal Processing Subsystem Command and Control Subsystem Time and Position Subsystem Storage Subsystem Tracking Subsystem Front End (Harder Real-Time) Back End (Softer Real-Time)
  • 16. Point to Point Communications Architecture © 2013 RTI Receiving Subsystem Transmitting Subsystem Tracking Subsystem Command and Control Subsystem User Subsystem Signal Processing Subsystem Time and Position Subsystem Cooperating System Storage Subsystem ?
  • 17. Data Centric Communications Architecture © 2013 RTI Receiving Subsystem Transmitting Subsystem Tracking Subsystem Command and Control Subsystem User Subsystem Signal Processing Subsystem Time and Position Subsystem Cooperating System Storage Subsystem TIME SENSOR DATA TRACKS STATE POSITION COMMAND DISPLAY DDS DDS
  • 18. Non-real-time Soft real-time Hard real-time Extreme real-time Java/RMIJava/JMS CORBA MPI Java RTSJ (soft RT) RTSJ (hard RT) Web Services MessagingTechnologiesandStandards Data Distribution Service / DDS RT CORBA Adapted from NSWC-DD OA Documentation RTI Data Distribution Service spans a very wide spectrum of application needs © 2015 RTI
  • 19. Open Systems • Open Systems – Are defined sufficiently that so that multiple organizations can work cooperatively on the same or separate sub- components – Have requirements which are stable over a sufficient length of time to allow for concurrent development – Are documented fully and openly to the development community – Are not under the control of any one firm or vendor. © 2015 RTI
  • 20. © 2015 Real-Time Innovations, Inc.20 Key Non-Functional Requirements for a System • Interchangeability (Portability) • Replaceability • Extensibility • Integratability System System A System B System System B System C F(A,B) Results in X F(C,B) Results in X A and C provide Equal Capability
  • 21. © 2015 Real-Time Innovations, Inc.21 Key Non-Functional Requirements for a System • Interchangeability • Replaceability • Extensibility • Integratability System System A System B System System B System C F(A,B) Results in X, Y, Z F(C,B) Results in Y, Z, W C is NOT an Equal Capability, but it Is a suitable substitute
  • 22. © 2015 Real-Time Innovations, Inc.22 System Key Non-Functional Requirements for a System • Interchangeability • Replaceability • Extensibility • Integratability System System B System C F(A,B) Results in X F(A,B,C) Results in X and Y System A System System B System A System C F(C) Results in Y © 2015 RTI
  • 23. © 2015 Real-Time Innovations, Inc.23 System C Key Non-Functional Requirements for a System • Interchangeability • Replaceability • Extensibility • Integratability System B F(A) Results In X F(A,B) Results in Z, where Z=G[f(X), g(Y)] System A System B System A F(B) Results in Y © 2015 RTI
  • 24. Infiniband Subsystem B Data Centric Integration Solution 1/30/2015 24 • Technical Interoperability – Infrastructure & Protocol • Syntactic Interoperability – Common Data Structure • Semantic Interoperability – Common Data Definition TRACKS STATE COMMAND DISPLAY DDS DDS Shared Memory Subsystem A TIME SENSORDATA STATE POSITION COMMAND DISPLAY DDS DDS IPv6 Subsystem C TIME SENSOR DAAA TRACKS STATE POSITION COMMAND DISPLAY DDS DDS TIME STATE POSITION DISPLAY DDS DDS Mediation Mediation Mediation IPv4 © 2015 RTI
  • 25. Core Architecture Built on Standard and Open Interfaces RTI Connext DDS Professional, Micro or Cert Operating System (Linux, LynxOS, Windows, VxWorks, QNX, ….) Optional FACE Transport Services API to DDS Mapping 25 Intra- process Shared memory ARINC Ports Sockets Other/ Custom RTITSSLibrary FACE Transport Services (TS) API RTI transport API OMG DDS API DDS-RTPS protocol Pluggable transports © 2015 RTI
  • 26. Optimized, Location-Independent Communication • Physical transport(s) configurable at integration time • Applications can use multiple transports concurrently • Transport(s) configured per application 26 Transport Use Intra-process Within the same address space (process) Shared memory Between processes in the same partition ARINC ports Within a node; within or between partitions Sockets (UDP unicast or multicast) Within or between nodes, including over Ethernet Low-bandwidth Over satellite or radio links (no IP requirement) Custom Over custom networks or busses (via plug-in API) © 2015 RTI
  • 27. Connection Mechanism Comparison 27 RTIDDS CORBA Sockets POSIX Queues Shared memory Queuing ports Sampling ports Proximity Intra-partition ● ● ● ● ● ● ● Inter-partition ● ● ● ● ● Inter-node ● ● ● Multiple concurrently ● Distribution One-to-one ● ● ● ● ● ● ● One-to-many ● ● ● ● ● Many-to-one ● ● ● Many-to-many ● ● ● Unreliable © 2015 RTI
  • 28. DDS Design Patterns How do they apply? © 2015 RTI
  • 30. Mediation : RTI Routing Service 30 Custom-DDS Translate – Routing Service Custom Plugin-In DW Mode: ON_DOMAIN Mode: ON_ROUTE <<input>> <<output>> <<participant_1>> <<participant_2>> Route Data Flow DDS-DDS Translate – Routing Service DR DW Mode: ON_DOMAIN Mode: ON_ROUTE <<input>> <<output>> <<participant_1>> <<participant_2>> Route Data Flow DDS/RTPS Source DataDDS/RTPS Source Data © 2015 RTI
  • 31. © 2015 Real-Time Innovations, Inc.31 Data Distribution Design Patterns TIME SENSOR DATA TRACKS STATE POSITION COMMAND DISPLAY DDSDDS Objective/ State One-to- Many High Throughput
  • 32. © 2015 Real-Time Innovations, Inc.32 Objective/State Design Pattern 3 States: 1. Current 2. Objective 3. Requested Objective 2+ Roles (special case of Observer pattern): 1. Effector • Provides Current State and Objective State • Observes Requested Objective State 2. Requester • Provides Requested Objective State • Observes Current State and Objective State 3. (Observer—with respect to any state) Current State Objective State change Effector executes this Requested Objective State Requester changes this 32
  • 33. Objective/State with DDS © 2012 RTI • COMPANY CONFIDENTIAL RequesterEffector  Durability QoS policy: – Current, Objective: Transient_Local – (Requested) Objective: Volatile  History QoS policy: – Current, Objective: Keep Last n – Requested Objective: Keep Last 1 Data Bus if long-running if short-running Current State write Objective State write Requested Objective State read, filter request processed? feedback Current State read, filter Objective State read, filter same typesame or different types same key Requested Objective State write
  • 34. © 2015 Real-Time Innovations, Inc.34 One-To-Many Design Pattern  Observation: More common in naturally data-centric interactions – “Hey, look at this” vs. – “Hey you: do this” Consumer Consumer Consumer … Producer 1 2 n
  • 35. © 2015 Real-Time Innovations, Inc.35 One-To-Many : Multicast Benefits • Communicate to many consumers at the same time much more cheaply than one-to- one to each of them – Less network traffic – Lower latency (fewer socket sends) – Lower writer-side CPU • Can be reliable or “best effort” – Configurable using DDS Quality of Service
  • 36. © 2015 Real-Time Innovations, Inc.36 One-To-Many : Multicast Challenges One-to-many reliability isn’t free Actual RTI multicast results Number of Readers 200-ByteSamplesperSecond Why? 1. Slow consumers throttle writer 2. Reliability bandwidth overhead Unicast expectation Perfect multicast expectation
  • 37. High Throughput Design Pattern • Do samples arrive continuously or at a high periodic rate? • Is the transport saturated?
  • 38. High Throughput Periodic Data • RTI Connext… – Sends synchronously by default – Supports batching for high periodic rates – Supports multiple reliability paradigms – Supports receive processing in receive thread or application thread
  • 39. High Throughput over Constrained Network • RTI Connext… – Supports configurable MTU sizes – Supports batching in a manner with reduces protocol header overhead – Supports a Low-Bandwidth network plugin with header and data compression – Supports a “multi-channel” feature to send data over different NICs as a function of data content
  • 40. Reliable High Throughput • Lots to consider – Writer must keep data for potential retransmission – Latency unpredictability – Readers must behave – Design for desired behavior if data lost… • Declare failure and stop • Report error and keep going • Delay writing for readers to catch up • Do nothing • …
  • 41. The Data Matters How do you Define & Design it? © 2015 RTI
  • 42. MODEL A model is anything used in any way to represent something else 42© 2015 RTI
  • 43. DATA MODEL A data model is a representation that describes the data about the things that exist in your domain 43© 2015 RTI
  • 44. Model and Implementation • Model provides the Context and Semantics – Containment and relationships – May not necessarily be in the messages • Messages can be compact – Use the model for context – ‘Know’ the association between a command and a status • Using machine readable context – Can generate the system appropriate mediation – Really only need the ID of ‘what’ in the message I © 2015 RTI
  • 45. DDS Natively Supports Interoperable Data Models • DDS messages are strongly typed • OMG IDL basis for native DDS Data Model schema – XML, XSD, also supported – Apps use target code generated by RTI’s IDL compiler • DDS natively understands data – Type safety – Heterogeneous interoperability (languages, CPUs) – Wire efficiency (minimizes metadata) – Enables middleware-level filtering (including at source) – Eases integration (explicit interfaces) 45 Platform Data Model RTI IDL Compiler C C++ Java Ada Include in application source © 2015 RTI
  • 46. Summary What were those two things, anyway? © 2015 RTI
  • 47. Create an Architecture Consistent with Life Cycle • Radar systems are often extremely long-lived – Much longer than consumer product life-cycle – Actively design for change with Data Centric architecture • Anticipate Multiple Technical Refresh cycles – Open architectures and standards are key to cost containment – Know your data I © 2015 RTI
  • 48. Focus on Domain Expertise • Mechanical Design • Algorithm Design • Custom Hardware Design • Compute plant and communications are areas of constant change – Communication Middleware isolates system-specific software from processor and network changes – Changes inevitable over system life-cycle I © 2015 RTI
  • 49. Start using DDS Today! Download the FREE complete RTI Connext DDS Pro package for Windows and Linux: • Leading implementation of DDS • C, C++, C#/.NET and Java APIs • Tools to monitor, debug, test, visualize and prototype distributed applications and systems • Adapters to integrate with existing applications and IT systems

Editor's Notes

  1. Interoperability and Open Architecture Current practice but… What is it really? Why is it hard?
  2. This illustration was originally created by the U.S. Navy, who chose NDDS for its Open Architecture Computing Environment. Of all the software, only NDDS covers the full range of performance requirements from non-real-time to extreme real-time. Non-real time: business layer apps Soft real time: C2, display & decision support Hard real time: e.g. sensor & weapon control Extreme real-time: e.g. signal processing
  3. A data-centric integration solution to achieve semantic interoperability is important and achievable. It is important because… One of the only things I can guarantee in a SOS is that it will change. At some point, it will. And when that change happens, rather than have your system be broken by it, why not survive it? If I architect my data in a rigorous and formal manner, and since data is what the systems operate on, then any changes in the system are easily accommodated, because they’d manifest as changes to the information present in the SOS. If the changes are made in a rigorous and repeatable way, then by knowing the rules for formation and the abstract data model that all things in the SOS come from, I can simply transform it and understand it if need be. The data will have meaning. It will have context. It will be usable and understood.   Letting your system be broken by something that is inevitable seems a bit silly, especially since we can anticipate that change and accommodate it by making some intelligent architecture and design decisions upfront. Here we can see legacy, future and current systems – which is a reality – they can technically interoperate via a protocol using a common infrastructure. We know how to do that. They can syntactically interoperate by using a common data structure. But how do we accommodate the systems where can can’t change the interfaces? When they are incompatible? We need a mediation component. Achieving semantic interoperability relies on components such as this, especially since one of our requirements was that we needed to be able to accommodate change and not be broken by it (have to make changes to existing interfaces).
  4. The TSS includes a mapping of the TS API to DDS, per the FACE Technical Standard. Standard and open interfaces: TS API RTI Transport API (called NETIO) DDS-RTPS wire protocol FACE OS security profile Internally DDS API
  5. A model is anything used in any way to represent something else. We use models to observe the effect on manipulating the original, without actually having to manipulate it. A really good model will capture all of the details we need to manipulate the original, and no more.   On the left we have a picture of an actual 1967 ford mustang gt, and on the right a model of that same car. Let’s say you have a child that is going through a phase where they’re really into cars. And this child wants nothing more than what his dad has – a 1967 ford mustang gt. Now, I love my kids and I want to give them everything just so I could see what marvelous things they’d do with it. However, I am not about to hand over the key to a car to my toddler. I would give them a scaled, fit for purpose version, such as the model toy on the right. It has very little in the way of extras, but it is entirely sufficient AND safe to entertain my toddler.
  6. A data model is a representation that describes the data about the things that exist in your domain.   If you have a system – since systems operate on data – well, then you have a data model. If you’re a system integrator, you deal with data models during your integration activities. Data models come in many different representations, they express many different things in varying degrees of explicitness. Some data models capture information very unambiguously and others don’t. But no matter where your data model falls on the spectrum, you can work with it to make it better.   Data models come in many flavors, and they’re not all equal. Which is best for you is going to depend on your systems requirements, and the function of the system, or component that will use that data. Here we have three examples of models many people have some familiarity with at least two of them. The dictionary is a list of terms for a particular domain of knowledge. It contains a list of terms, as well as the definitions and pronunciations for those terms. Using a representation such as this, words alongside their meaning, we can communicate about the things that exist in our domain and the meaning of those expressions, the words, is understood to those who use the same dictionary. The linnean taxonomy is an example of a hierarchical data model - it shows us the conception, naming, and classification of organism groups. It represents information in a hierarchical format, such a classification or categorization schema. Using a representation structure such as this, I can express that “this” is one of “those”. The last example is the periodic table of elements. From this we can tell that Gold has a weight and a certain number of protons… but I don’t know if 2 elements will bond, and if they will what they will form, simply by looking at this table.   Per our requirements, we define a good data model to be one that captures, among other things, the semantics, or meaning, of the things that is represents in an unambiguous way. The process by which you generate a data model is something you need to consider… Especially if you need that data model that helps you meet your key non-functional requirements.