Real-Time Calculus (RTC) is a powerful framework to analyze real-time performance of distributed embedded systems. However, RTC may run into serious analysis efficiency problems when applied to systems of large scale and/or with complex timing parameter characteristics. The main reason is that many RTC operations generate curves with periods equal to the hyper-period of the input curves. Therefore, the analysis in RTC has exponential complexity. In practise the curve periods may explode rapidly when several components are serially connected, which leads to low analysis efficiency. In this work, we propose Finitary RTC to solve the above problem. Finitary RTC only maintains and operates on a limited part of each curve that is relevant to the final analysis results, which results in pseudo-polynomial computational complexity. Experiments show that Finitary RTC can drastically improve the analysis efficiency over the original RTC. The original RTC may take hours or even days to analyze systems with complex timing characteristics, but Finitary RTC typically can complete the analysis in seconds. Even for simple systems, Finitary RTC also typically speeds up the analysis procedure by hundreds of times. While getting better efficiency, Finitary RTC does not introduce any extra pessimism, i.e., it yields analysis results as precise as the original RTC.
