Achieve fault tolerance with a real-time software design

Data Distribution Service (DDS) specification from Object Management Group (OMG) is a data-centric publish/subscribe (DCPS) messaging standard for integrating distributed real-time applications. Here’s how process replication can increase a system’s fault tolerance.


The most basic approach is to run multiple, identical Manager applications. Courtesy: Real-Time InnovationsData Distribution Service (DDS) specification from Object Management Group (OMG) is often used to build mission critical systems consisting of multiple components executing simultaneously and collaborating to achieve a certain task. In such systems, there are usually several components that are critically important for the overall functioning. Those components require extra attention when designing the system to ensure that the likelihood of them failing is minimized. Its focus on reliability, availability and efficiency make the DDS infrastructure particularly suitable to connect the plant floor with other areas of the enterprise.

DDS provides advanced features that help achieve such goals. While there are many methods, fault tolerance for failing publishing applications or machines can be achieved by means of active process replication.

Guiding example

For the purpose of illustrating the theory here with an example, the use case of the simple distributed finite state machine (FSM) is described. The FSM consists of a few components: a Master Participant, a Worker Participant, and a Manager. The Observer component, not essential for the functionality of the pattern, is passively observing only. This example focuses on the interaction between the Manager and the Observer.

Let’s assume that the Manager is the most critical process in the pattern, which is likely to be the case, especially if the pattern is expanded to support multiple Masters and Workers. The most obvious way to make the Manager component more robust is by running multiple processes simultaneously, all with the same responsibility. With that approach, the Manager component is defined as a conceptual item that may consist of multiple, simultaneously running application processes. For the sake of simplicity, the collection of those processes is referred to as the Manager component. Note that those could be executing in a distributed fashion on multiple machines.

Active process replication

Simultaneously running multiple Manager processes with the same purpose will decrease the likelihood that the Manager component will break down completely. DDS allows for adding and removing participants on the fly without requiring configuration changes, so the basic concept of process replication is supported. Still, there are multiple options from which to choose.

Plain process replication

To avoid disadvantages, another option is to make Manager processes aware of each other at the application level. Courtesy: Real-Time InnovationsThe most basic approach is to run multiple, identical Manager applications. Each acts the same, receiving the same transition requests and publishing the same state updates in response. As a consequence, applications observing the state machine will see multiple instances of that state machine simultaneously. This approach requires a minor adjustment to the data-model to allow multiple task instances to exist side-by-side. This is achieved by adding a key attribute that identifies the originating Manager application.

An advantage to this approach is its low complexity; just replicating everything is an approach everybody understands. There is no impact on the Manager application code, and all participants remain completely decoupled from each other. Additionally, the mechanism does not rely on any built-in replication mechanism from the middleware. This could be considered an advantage as well, because the developer now has every aspect of the replication process under control.

On the other hand, that latter argument could be seen as a disadvantage. Requiring all Observers of the state machine to be able to deal with multiple, identical versions simultaneously does have an impact on the application code and introduces an extra burden on the application developer. Also, the number of instances an instance state updates scales linearly with the number of Manager processes. For this particular state machine example that is not that big of a deal, but in the general case, it could have a significant impact on network bandwidth consumption and memory usage.

<< First < Previous 1 2 Next > Last >>

No comments
The Engineers' Choice Awards highlight some of the best new control, instrumentation and automation products as chosen by...
The System Integrator Giants program lists the top 100 system integrators among companies listed in CFE Media's Global System Integrator Database.
The Engineering Leaders Under 40 program identifies and gives recognition to young engineers who...
This eGuide illustrates solutions, applications and benefits of machine vision systems.
Learn how to increase device reliability in harsh environments and decrease unplanned system downtime.
This eGuide contains a series of articles and videos that considers theoretical and practical; immediate needs and a look into the future.
Choosing controllers: PLCs, PACs, IPCs, DCS? What's best for your application?; Wireless trends; Design, integration; Manufacturing Day; Product Exclusive
Variable speed drives: Smooth, efficient, electrically quite motion control; Process control upgrades; Mobile intelligence; Product finalists: Vote now; Product Exclusives
Machine design tips: Pneumatic or electric; Software upgrades; Ethernet advantages; Additive manufacturing; Engineering Leaders; Product exclusives: PLC, HMI, IO
This article collection contains the 5 most referenced articles on improving the use of PID.
Learn how Industry 4.0 adds supply chain efficiency, optimizes pricing, improves quality, and more.

Find and connect with the most suitable service provider for your unique application. Start searching the Global System Integrator Database Now!

Cyber security cost-efficient for industrial control systems; Extracting full value from operational data; Managing cyber security risks
Drilling for Big Data: Managing the flow of information; Big data drilldown series: Challenge and opportunity; OT to IT: Creating a circle of improvement; Industry loses best workers, again
Pipeline vulnerabilities? Securing hydrocarbon transit; Predictive analytics hit the mainstream; Dirty pipelines decrease flow, production—pig your line; Ensuring pipeline physical and cyber security