Webcast (sorry, audio not included) on system integration design patterns from July of 2010 pertaining mostly (but not exclusively) to Data-Distribution Service (DDS) technology.

1. The Real-Time
Middleware Experts
Easing Integration of Large-Scale
Real-Time Systems with DDS
Rick Warren, Principal Engineer rick.warren@rti.com

2. Introduction
 Rick Warren
– Principal Engineer; with RTI since 2001
– Engineering organization; previously Professional Services
– Together with CTO, lead RTI’s Object Management Group
(OMG) initiatives
– Passionate about systems integration, standards, and usability
 This Presentation
– Large-scale design patterns for integrating heterogeneous
distributed systems
– Part 1: Architecture and requirements
– Part 2: Addressing Part 1 with OMG DDS
© 2010 Real-Time Innovations, Inc. 2

3. RTI: The Global Leader in DDS
 70%+ worldwide DDS market share
 First with…
– DDS API (2004)
– DDS-RTPS interoperability protocol
(2007) — based on RTI’s native protocol
 Most mature solution
– 12+ years of commercial availability
– Technology Readiness Level (TRL) 9:
successful mission operations
– Diverse range of industries: defense, finance, medical,
industrial control, power generation, communications
– 300+ commercial customers, 100+ research projects
– 100,000+ licensed copies
 Leading OMG standardization
– Board of Directors member
– Co-chair DDS Special Interest Group
– Chair several DDS standard finalization, revision task forces
© 2010 Real-Time Innovations, Inc. 3

4. © 2010 Real-Time Innovations, Inc. 4
Customer Application Highlights
Weapon System
Lockheed Martin Aegis
Radar, weapons, displays, C2
Unmanned Air System
Boeing ScanEagle
Sensors, ground station
Control System
Grand Coulee Dam: largest
electricity producer in US
Controls, high availability
Driver Safety
Volkswagen
Vision systems, analysis,
driver information systems
Medical Imaging
NMR and MRI
Sensors, RF generators, user
interface, control computers
Automated Trading
Automated Trading Desk
(ATD, now Citigroup)
Market data feed handlers,
pricing engines, algorithmic
trading applications

5. What do I mean by “large system”?
 Systems of systems
– Modular, hierarchical design
– Legacy components,
subsystems
– Multiple technologies
– Global scale
 Decoupled subsystem
lifecycles
– Independent development
– Independent deployment
and use
– Independent management
– Independent revision and
retirement
 Multiple communities of
interest
– Different data interest,
entitlements
– Non-uniform levels of trust
© 2009 Real-Time Innovations, Inc. 5
LAN
DDS #1
(Initech)
JMS
(Dunder
Mifflin)
DDS #2
(Acme)
Legacy
(COBOL ‘R’
US)
Satellite
Links

6. What matters to integrators of large systems?
 Governance (including over security)
– What information will be exchanged?
– Under what conditions will it be exchanged?
– Who is allowed to exchange this information?
– If these SLAs are violated, can the exchange be prevented?
Can I be notified?
– (In the past, what has occurred wrt these SLAs?)
 Isolation
– When I connect A and B, ensure they don’t break (each other)
– When I disconnect A, ensure B doesn’t break
– When I connect C, don’t change A or B
 Scalability (like in any system,
 Fault Tolerance but stakes are higher)
© 2009 Real-Time Innovations, Inc. 6

7. Part 1: Architecture
(we’ll get to technology later)
© 2010 Real-Time Innovations, Inc. 7

8. System
Component
Component
Component
Class
A Modest Proposition
© 2009 Real-Time Innovations, Inc. 8
State
Behavior
Class
 Fundamental design principles scale
– Abstraction: Provide interface based on relevant concepts
– Encapsulation: Hide internal implementation, communication
– Composition: Combine existing capabilities into new ones

9. Subsystem 2
P P
PP P
Subsystem 1
P P
PP P
Schematic of a Composed System
 Subsystems may have different network environments
 Integration may have different network environment
than subsystems themselves
 Data may need to be transformed / cleansed as it
moves among subsystems
 Routing / gateway services will adapt data types /
formats / protocols
LAN LAN
WAN
Router/G
ateway
Router/G
ateway
Isolation
Additional
Governance

10. Schematic of a Composed System
 Wash, rinse, repeat
Subsystem 1
P P
PP P
Subsystem 2
P P
PP P
Router/G
ateway
Router/G
ateway
© 2010 Real-Time Innovations, Inc. 10

11. Same data model?
Same network env.?
Same lifecycle?
Behavior unaffected?
Understandable?
Apples and Oranges
P P
PP P
Subsystem 1 + Subsystem 2
P P
PP P
Subsystem 2
P P
PP P
Subsystem 1
P P
PP P
Router/
Gateway
Router/
Gateway

12. Nothing New Under the Sun
© 2009 Real-Time Innovations, Inc. 12
Subsystem
Data Space
Subsystem
Data Space
Subsystem
Data Space
Integration
Data Space
Router/
Gateway
Router/
Gateway
Router/
Gateway

13. Nothing New Under the Sun
© 2010 Real-Time Innovations, Inc. 13
U.S. Combat
Data Space
Allied Combat
Data Space
U.S. C4I
Data Space
Integration
Data Space
Router/
Gateway
Router/
Gateway
Router/
Gateway
Defense
Industry

14. Nothing New Under the Sun
© 2010 Real-Time Innovations, Inc. 14
NYC Trading
Data Space
London Trading
Data Space
Tokyo Trading
Data Space
Integration
Data Space
Router/
Gateway
Router/
Gateway
Router/
Gateway
Financial
Services
Industry

15. Nothing New Under the Sun
© 2010 Real-Time Innovations, Inc. 15
Generation
Data Space
Substation #1
Data Space
Substation #2
Data Space
Integration
Data Space
Router/
Gateway
Router/
Gateway
Router/
Gateway
Power
Industry

16. Architecture Recap
 Solve big problems
like you solve small
ones
– Break them into
pieces
– Define interfaces
between pieces
– Implement each piece
– Integrate along the
interfaces
 Translation
– Define subsystems
– Implement them
however you like
• May have different
requirements, therefore
technology
– Router/gateway
defines/enforces
interfaces
© 2010 Real-Time Innovations, Inc. 16

17. Fault Tolerance and Scalability
 Fault tolerance: Router/gateway can’t be single point of
failure. 3 answers:
1. Persistent data survives system failures
2. Segmented/load-balanced configuration limits scope of failures
3. Redundancy allows continued service during failure
 Scalability: How big can a subsystem be?
1. How big a subsystem can you understand, test, and debug?
2. How well does infrastructure scale?
© 2010 Real-Time Innovations, Inc. 17

18. Part 2: DDS
© 2010 Real-Time Innovations, Inc. 18

19. DDS Is Open Architecture
 OMG standard for data-centric
publish-subscribe communication
 Vendor independent
– API for portability
– Network protocol for interoperability
 8+ implementations
– Commercial, open-source, and
hybrid licenses
 Supports heterogeneous
systems
– C, C++, C#/.NET, Java, Ada, …
– Windows, Linux and other Unix,
embedded, RTOS
Real-Time
Publish-Subscribe
Protocol (DDS-RTPS)
Middleware
DDS API
Cross-vendor portability
Cross-vendor interoperability
© 2010 Real-Time Innovations, Inc. 19

20. Example: Data-Centric Track Data
© 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 20
Publish
Subscribe
Data Schema
x : float
y : float
id : string (key)
New
45.6
78.9
“AA123”
Update
56.7
89.0
“AA123”
New
65.4
32.1
“DL987”
Dispose
“AA123”
X
Map this into XML; rows + cols
Express content-based filters
Propagate data efficiently

21. Example: Data-Centric Track Data
© 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 21
Publish
Subscribe
Data Schema
x : float
y : float
id : string (key)
Quality of Service
Deadline
Time-Based
Filter
History
 Once infrastructure understands objects, can attach
QoS contracts to them
 “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

22. Analysis by U.S. Armed Services
NESI, P1190 rev 3, http://nesipublic.spawar.navy.mil/nesix/Frames/P1190
© 2010 Real-Time Innovations, Inc. 22
Adapted from NSWC-DD OA Documentation

23. How Does DDS Stack Up?
 Governance
– Structural SLA: data type
– Behavioral SLA: delivery QoS
– Security
– Problem
detection/prevention/notification
– System monitoring and recording
 Isolation
– Prevent data “leaks” and side
effects
– Allow dynamic (dis-|re-)connection
– Support independent integration
and evolution
 Fault tolerance
– Data persistence
– Segmented/load-balanced routing
– Redundant routing
 Scalability
– WAN, DIL connectivity
– Peer-to-peer communication
– Brokered/managed communication
– Built in
– Built in
– Products available; future standards
– Built in
– Products available
– Built in: domains, partitions
– Built in
– Built in, esp. w/ DDS-XTypes spec
– Built in
– Built in if router uses DDS
– Built in if router uses DDS
– Products available; future standards
– Highly scalable
– Protocol support if necessary
© 2010 Real-Time Innovations, Inc. 23

24. DDS Scalability: Two Aspects
1. Application data
– How does performance fall off as # participants, writers,
readers increase?
– Any single writer/reader pair can saturate gig-E:
achieving high aggregate throughput is not main issue
– Interesting issue: Fan-out – number of readers per writer
2. Discovery
– How many DDS applications can discover one another?
– Interesting issue: How many applications need to discover
one another?
 Hard limits, if any, very dependent on DDS implementation,
machine configuration, network, etc.
– Following results based on RTI on modern commodity HW
© 2010 Real-Time Innovations, Inc. 24

25. 1. DDS Scalability: Application Data
 DDS-RTPS reliable multicast scales at least:
– …to hundreds of readers per writer
– …with very little degradation in throughput.
– Larger testing facilities welcomed. 
© 2010 Real-Time Innovations, Inc. 25
< 15% decrease
1~900 readers
RTI Data Distribution Service 4.3
Red Hat EL 5.0, 32-bit
2.4 GHz processors
Gigabit Ethernet
UDP/IPv4
Reliable ordered delivery

26. 2. DDS Scalability: P2P Discovery Scenarios
 Single domain, symmetric discovery
– Everyone discovers everyone else
– Easiest to configure; most challenging wrt scalability
– RTI test results: 1,800 participants; 3.2M endpoint matches
 Single domain, asymmetric discovery
– Each participant only knows of certain others in its domain
– Good for partitioning domains in which not everyone talks
 Multiple domains
– Greatest separation for subsystems that rarely exchange data
• DDS-RTPS maps to different IP ports
© 2009 Real-Time Innovations, Inc. 26
1
…
n

27. Summary: Large-Scale P2P Scalability
 Experimental results: DDS-RTPS allows participant to
talk to ≥ 1-2K others in single symmetric domain
 Implication: Compose arbitrarily large systems out of
subsystems this size or smaller
 Large (sub)systems often have requirements for:
– Increased governance, e.g. over security boundaries
– Data transformation
– Protocol mediation
– These typically require data brokering anyway
© 2009 Real-Time Innovations, Inc. 27

28. Geographic Scalability: WAN Transport
 Site-to-site comms need DDS across WAN(s)
– Including untrusted networks
– Including firewall, NAT traversal
– Challenge: UDP may not be routable, especially if multicast
 Standard DDS-RTPS protocol designed for layering
atop diverse lower-level protocols
– RTI supports:
• Shared memory for performance on a single machine
• UDP (unicast, multicast) for scalability, determinism w/in LAN
• TCP for routing across WAN
• (D)TLS for transport-level security
• etc.
• …simultaneously
© 2010 Real-Time Innovations, Inc. 28

29. Geographic Scalability: DIL Transport
 DDS is also great on disadvantaged networks
– DDS QoS provide tunable behavior, performance
– DDS-RTPS protocol encapsulates data efficiently w/ CDR
© 2010 Real-Time Innovations, Inc. 29
Source:
• SELEX-SI/
Consorzio SESM/
University of Naples
• “Flexible
Communication
Among DDS
Publishers and
Subscribers”
• July 2008, Real-
Time Systems
Workshop,
Washington, DC
10x more compact
than XML or JSON

30. Geographic Scalability: DIL Transport
 DDS is also great on disadvantaged networks
– DDS QoS provide tunable behavior, performance
– DDS-RTPS protocol encapsulates data efficiently w/ CDR
© 2010 Real-Time Innovations, Inc. 30
Source:
• SELEX-SI/
Consorzio SESM/
University of Naples
• “Flexible
Communication
Among DDS
Publishers and
Subscribers”
• July 2008, Real-
Time Systems
Workshop,
Washington, DC20x faster

31. RTI Routing Service:
Integrating Non-DDS Applications
 Adapt to other protocols and interfaces
 Adapter SDK with customizable plugins
– JMS
– Socket
– File
 Take advantage of Routing
Service capabilities
– Data transformation and filtering
– Data life-cycle management
– Cross networks, transports
and platforms
– Dynamic discovery
DDS
Plug-in
Adapters
© 2010 Real-Time Innovations, Inc. 31

32. Recap
 Large systems usually composed of smaller systems
– Developed independently
– …With different network environments
– …And different data interest/entitlements
 Not an accident: hierarchical design is a best practice
at any scale
– Subsystems managed by router/gateway
• Restrict access to internal topics/flows
• Mediate internal/external protocols
• Transform/cleanse internal/external data types
• Impose governance: enforce policy, attach monitoring
– Inter-router integration space is itself a “subsystem” for further
hierarchical composition
© 2010 Real-Time Innovations, Inc. 32

33. Recap
 DDS is uniquely qualified to meet these needs
– Strong SLAs for governance
– Multiple topologies for fault-tolerant subsystem isolation
– High-performance, scalable, deterministic interoperability
in various network environments
– What other standard technology offers this combination?
(Not JMS. Not CORBA. Not AMQP. Not web services. …)
© 2010 Real-Time Innovations, Inc. 33

34. Next Steps
 Free software downloads
– www.rti.com/downloads
– Product evaluation:
full RTI Professional Edition
• Free trial with comprehensive tutorial
• Free licenses for research & academia
• Includes:
– Leading DDS implementation
– Data recording
– System monitoring and debugging
– Integrates with JMS, databases,
Microsoft Excel
– More
– Shapes demo: no coding required
 Videos, webinars, and whitepapers
– www.rti.com/resources
© 2010 Real-Time Innovations, Inc. 34