DDS: A Flexible Data-Oriented Architecture
A more flexible, scalable and adaptable approach is using a middleware technology called Data Distribution Service. It has been defined as a standard for network middleware with the Object Management Group (OMG) and officially termed the Data Distribution Service for Real-Time Systems (DDS) standard.

This standard provides for the use of a publish-subscribe communication model that enables data producers to autonomously publish data to a smart-data infrastructure and allows data consumers to subscribe to data from this data infrastructure.

The data is abstracted away from the source and made accessible to any application that subscribes to it, independent of the source's location and the specific link technology that transports the data.

DDS enables a resiliency of data necessary for fault tolerance across the network. There are commercial implementations of the OMG DDS standard that adhere to published specifications while also offering high performance for specific distributed systems and applications.

The standard also supports a high-performance real-time response for data exchange, as well as QoS parameters such as reliability, durability, deadline, priority and data ownership. By adjusting QoS parameters, system and application software developers will be able to ensure that the transmission and reception of data meets the unique needs of each system and application.

The rich QoS parameter set makes it possible to implement DDS on a wide range of processors and networks, including those that are embedded. Commercial off-the-shelf (COTS) systems that have implemented the OMG DDS standard have been used in a growing number of defense systems projects with success.

This combination makes possible a robust data communications infrastructure for a UAV. Consider a data-driven design based on abstracting data away from the code acting on it and placing the data in known locations on the network. Such an infrastructure has the advantages of direct connections between subsystems—without the corresponding disadvantages.

It is possible to think of the DDS infrastructure as a logically shared data bus where data producers and consumers post and request data. From the point of view of the system designer, the data bus implements the details of getting data from one component to another in real time and with an acceptable QoS.

The design problem is abstracted to determining data needs for components and determining the appropriate QoS. The DDS infrastructure acts as the data bus that transports the data from source to consumer.

Figure 3: The DDS infrastructure manages the transport of data from one component to the other.

How does a data-oriented architecture address the need for direct interconnections between individual subsystems? Actually, it provides the same end result, albeit by a different method.

Rather than passing data directly between hardwired subsystems, the DDS infrastructure enables subsystems and individual code components to publish - or post - data to a logical location on the network. Other subsystems subscribe to that data.

In effect, the data distribution middleware makes the decisions about the mechanics of getting data from one location to the other. The actual transmission of the data is managed by the middleware. The application code only has to ensure that the data is published or that the data is subscribed to, rather than having to implement the mechanism.

Could it be simpler and more straightforward to code data transfer via direct connections? In that case, it would be necessary to design and code for not only the physical data transmission, but also response-time tolerances, failover and other QoS characteristics.

This alternative makes for large amounts of detailed code that must also be tuned for performance, an approach that is both time consuming and prone to error.

In contrast, configurable QoS is a primary advantage of the DDS standard. In particular, QoS parameters - such as delivery modes, acknowledgement response time, durability and lease duration - are necessary to make it possible to tune the software's performance and reliability to the underlying network technology.

Specific properties of low-bandwidth, unreliable wireless links as well as high-speed interconnects can be addressed in this way using a single communications infrastructure. Ideally, QoS would be matched to the latency and bandwidth requirements of each data stream to deliver data where it is needed, when it is needed.

Redundant data pathways are still required physically, but they need not correspond to logical point-to-point connections between subsystems. In fact, it is highly unlikely that any such direct connections would be needed.

Rather, the network architecture can be designed to optimize QoS characteristics such as latency, failover and data priority. The physical architecture of the network can be based on these characteristics, leaving it to the middleware to publish data, subscribe to data and manage the overall flow of that data.

Real-Time System Architecture Maintenance
No single UAV design can successfully meet all of the possible mission requirements for such vehicles. However, by applying a data-oriented architecture, a more flexible design results that can meet the needs of a variety of missions because it has optimized the tradeoffs inherent in design.

A data-oriented architecture reduces complexity by abstracting implementation details away from the code, letting developers write less code to achieve a more robust solution.

The OMG DDS standard and its commercial implementations make it possible to develop data-driven systems and software while also practicing abstraction. The result is less application code, improved system responsiveness and the ability to meet more mission parameters in actual use.

Dr. Edwin de Jong is Director of Product Management and Strategy, Core Products at Real-Time Innovations, Inc. He has more than 15 years experience in the architecture and design of large-scale distributed real-time systems in applications such as C4I, radar, track management, multi-sensor data fusion, threat evaluation, weapon and sensor assignment, and simulation and training. Edwin holds a Ph.D. in mathematics and physics from Leiden University, The Netherlands. Editor's Note: To learn more about high-performance messaging middleware download the RTI Shapes Demo.