Methods for Comparing Execution Times of Different Input Data when Real-Time Tasks Run on Complex Computer Architectures

Abstract

Real-time tasks executed on complex computer architectures suffer large interference from other activities executing on the same system, hence generating noise in the observed execution times. In this context, it is difficult or impossible to determine the worst scenario for tasks' measurement-based temporal analysis, i.e., the hardware state and execution path that generates the longest execution time. Both the hardware effects and the execution paths depend directly or indirectly on the input data used. In this work, we (1) performed an empirical assessment of different measurement methods, with the objective of identifying which ones enable comparing different input data with respect to the generation of longer execution times, (2) implemented a genetic algorithm in which the fitness function is based on execution time measurements selected using both traditional and novel methods, showing the relevance of the measurement method for input data analysis. The focus of the work is hence not on finding tasks' worst input data, but on measurement methods that allow reliably comparing tasks' execution times produced using different input data by suppressing complex computer architectures' noise from measurements.