Calculating WCET estimates from timed traces

Abstract

Real-time systems engineers face a daunting duty: they must ensure that each task in their system can always meet its deadline. To analyse schedulability they must know the worst-case execution time (WCET) of each task. However, determining exact WCETs is practically infeasible in cost-constrained industrial settings involving real-life code and COTS hardware. Static analysis tools that could yield sufficiently tight WCET bounds are often unavailable. As a result, interest in portable analysis approaches like measurement-based timing analysis is growing. We present an approach based on integer linear programming (ILP) for calculating a WCET estimate from a given database of timed execution traces. Unlike previous work, our method specifically aims at reducing overestimation, by means of an automatic classification of code executions into scenarios with differing worst-case behaviour. To ease the integration into existing analysis tool chains, our method is based on the implicit path enumeration technique. It can thus reuse flow facts from other analysis tools and produces ILP problems that can be solved by off-the-shelf solvers.

Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices

“…In the hybrid methodology, we apply a static analysis on the empirical results from the measurement-based layer to create a trade-off between computational complexity and accuracy. The final WCRC Predictions are acquired using an Implicit Path Enumeration Technique (IPET) on the CFG [43] that is generated from the block model using graph transformations. Each block type has a corresponding transformation that describes how it connects with its parent and children in the hierarchical block tree.…”