Statistical regression models for WCET estimation

Abstract

1. Problem Statement. Worst Case Execution Time (WCET) is the upper bound of the execution time of a real-time task running on a particular hardware platform. The estimation of WCET is of significant importance to the correctness of schedulability analysis and thus the reliability of real-time applications. 2. Approach. Traditionally, WCET is often estimated based on static analysis techniques. However, advanced architectural features make the static analysis of WCET extremely complicated. As a result, measurement-based approaches become popular in practice. Despite its popularity, this type of approach would require running a task with a large number of input settings to obtain safe and accurate WCET estimates. To solve this challenge, we develop statistical regression models on a set of benchmark tasks to connect task execution time with some selected features. Our models can be used to estimate the WCET of new tasks without running a large number of sample executions. 3. Results. Our fitted regression models show that the execution times are highly related to the selected feature factors ‘Load’ and ‘Missing Rate’. Through a comparison study, we evaluate different measurement-based approaches from both safe and accurate perspectives. The comparison results are summarized to give general guidelines in searching good measurement-based WCET estimation approaches.