In this paper, we provide first results introducing the impact of both software and hardware events on the estimation of worst-case energy consumption of programs on embedded processors. We build a framework to better understand the representativeness of measurements with respect to both software and hardware events. We test this framework on execution times and energy consumption data for 5 existing benchmarks as a step towards a statistical worst-case energy consumption model.

The real-time systems community is facing the lack of benchmarks adapted to measurement-based worst-case execution time (WCET) estimators. We provide in this paper first steps towards such benchmarks by proposing them for single core microcontrollers, while we leave as future work the migration to multicore microcontrollers. The considered benchmarks are the programs of an open source drone autopilot. We conclude the paper by underlining the main difficulties of such migration.

This paper presents the design and analysis of a data science competition on a problem of time series regression from aeronautics data. For the purpose of performing predictive maintenance, aviation companies seek to create aircraft “numerical twins”, which are programs capable of accurately predicting strains at strategic positions in various body parts of the aircraft. Given a number of input parameters (sensor data) recorded in sequence during the flight, the competition participants had to predict output values (gauges), also recorded sequentially during test flights, but not recorded during regular flights. The competition data included hundreds of complete flights. It was a code submission competition with complete blind testing of algorithms. The results indicate that such a problem can be effectively solved with gradient boosted trees, after preprocessing and feature engineering. Deep learning methods did not prove as efficient.

The increased complexity of programs and processors is an important challenge that the embedded real-time systems community faces today, as it implies substancial timing variability. Processor features like pipelines or communication buses are not always completely described, while black-box programs integrated by third parties are hidden for IP reasons. This situation explains the use of statistical approaches to study the timing variability of programs. Most existing work is concentrated on the guarantees provided by positive answers to statistical tests, while our current work concerns potential algorithms based on the negative answers to these tests and their impact on the timing analysis. We introduce here one such algorithm, the Walking Kolmogorov-Smirnov test (WKS).

The increased use of statistical libraries within the real-time community is facing today the lack of appropriate libraries. Many of them exist in R, but existing Python statistical libraries are lacking, particularily with respect to Extreme Value Theory and probabilistic worst-case execution time estimation, which are indispensable tools for the analysis of real-time systems. This short paper attempts to describe and bridge this gap.

Measurement-based Worst Case Execution Time (WCET) estimations require appropriate benchmarks that are composed both by programs and representative measurement protocols. The representativeness is defined with respect to both the programs and processors executing them. In this paper we consider the representativeness with respect to a multicore processor and we provide preliminary conclusions on how appropriate benchmarks could be proposed.

The use of increasingly complex hardware and software platforms in response to the ever rising performance demands of modern real-time systems complicates the verification and validation of their timing behaviour, which form a time-and-effort-intensive step of system qualification or certification. In this paper we relate the current state of practice in measurement-based timing analysis, the predominant choice for industrial developers, to the proceedings of the PROXIMA (Probabilistic real-time control of mixed-criticality multicore systems) project in that very field. We recall the difficulties that the shift towards more complex computing platforms causes in that regard. Then we discuss the probabilistic approach proposed by PROXIMA to overcome some of those limitations. We present the main principles behind the PROXIMA approach as well as the changes it requires at hardware or software level underneath the application. We also present the current status of the project against its overall goals, and highlight some of the principal confidence-building results achieved so far.