Predictability Of Real-time Systems Research Articles

A method to improve interrupt latency in real-time OS kernels

Interrupt latency has great impact on the real time and predictability of real-time operating system kernels. In traditional implementation of real-time kernels, interrupts are frequently disabled in system service routines, critical sections, etc., which causes kernel interrupt latency. In this paper we propose a method called PIL (Predictive Interrupt Latency). PIL reduces interrupt latency by never disabling interrupts during any system service routines. Moreover, it makes interrupt latency vary little, which improves the predictability in the response time for external events. We have implemented the PIL method in an OSEK-compatible operating system - SmartOSEK OS. Experimental results show PIL improves real time and predictability of real-time systems with low time and space overheads.

REAKT: A real time architecture for knowledge based systems

Expert systems have now reached maturity and an ever growing number of applications make use of this technology. Nevertheless, specific requirements of real time applications have significantly slowed down the use of these techniques in this area: this is first due to incompatibilities between time and memory requirements of AI algorithms and real time specificities, but also to a lack of adaptation to the growing complexity of these systems. In this paper a real time architecture for Knowledge Based Systems: REAKT (Real Time Knowledge Tool) which aims at combining the expressiveness and power of Knowledge Based Systems with the efficiency and predictability of real time systems is presented.

What is predictability for real-time systems?

In summary, predictability in real-time systems has been defined in many ways. For static real-time systems we can predict the overall system performance over large time frames (even over the life of the system) as well as predict the performance of individual tasks. If the prediction is that 100% of all tasks over the entire life of the system will meet their deadlines, then the system is predictable without resorting to any stochastic evaluation. In dynamic real-time systems we must resort to a stochastic evaluation for part of the performance evaluation. Predictability for these systems should mean that we are able to satisfy the timing requirements of critical tasks with 100% guarantee over the life of the system, be able to assess overall system performance over various time frames (a stochastic evaluation), and be able to assess individual task and task group performance at different times and as a function of the current system state. If all these assessments meet the timing requirements, then the system is predictable with respect to its timing requirements.