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.
Data 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.
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
The 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.
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