How do you manage concurrency in your embedded system?

1 Task types

A task is a unit of work that can be executed by the embedded system. Tasks can be classified into two types: periodic and aperiodic. Periodic tasks have a fixed frequency or deadline, such as reading a sensor every 10 milliseconds. Aperiodic tasks are triggered by external events or conditions, such as a button press or a network request. Periodic tasks are usually more predictable and easier to schedule than aperiodic tasks, but they may also have higher priority and stricter timing constraints.

2 Concurrency models

A concurrency model is a way of organizing and executing tasks in an embedded system. There are two main concurrency models: cooperative and preemptive. Cooperative concurrency means that tasks voluntarily yield the CPU to other tasks when they are done or waiting for something. Preemptive concurrency means that tasks can be interrupted and replaced by other tasks at any time, based on some criteria such as priority or deadline. Cooperative concurrency is simpler and more efficient, but it requires careful design and coding to avoid blocking or starving other tasks. Preemptive concurrency is more flexible and responsive, but it introduces more complexity and overhead, such as context switching and synchronization.

3 Synchronization mechanisms

Synchronization mechanisms are tools that help coordinate the access and communication of shared resources among concurrent tasks. Shared resources can be anything that can be used by more than one task, such as memory, peripherals, files, or data structures. Synchronization mechanisms can be divided into two categories: mutual exclusion and signaling. Mutual exclusion means that only one task can access a shared resource at a time, while others have to wait. Signaling means that a task can notify or wait for another task about some condition or event. Some common synchronization mechanisms are semaphores, mutexes, monitors, message queues, and event flags.

4 Scheduling algorithms

A scheduling algorithm is a rule that decides which task should run next on the CPU. A scheduling algorithm can be static or dynamic. Static scheduling means that the order and timing of tasks are determined at design time, and do not change at run time. Dynamic scheduling means that the order and timing of tasks are determined at run time, based on some factors such as priority, deadline, or resource availability. Some common scheduling algorithms are round-robin, rate-monotonic, earliest deadline first, and priority inheritance.

5 Performance metrics

Performance metrics are measures that evaluate how well an embedded system manages concurrency. Performance metrics can be related to the quality of service, the resource utilization, or the reliability of the system. Some examples of performance metrics are response time, throughput, jitter, CPU load, memory usage, and fault tolerance.

Managing concurrency in an embedded system is a complex and important topic that requires careful analysis, design, and implementation. By understanding the basic concepts and techniques presented in this article, you can start to develop your own concurrency solutions for your embedded projects.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?