Model-Driven Runtime Resource Predictions for Advanced Mechatronic Systems with Dynamic Data Structures

Abstract

The next generation of advanced mechatronic systems is expected to enhance their functionality and improve their performance by context-dependent behavior. Therefore, these systems require to represent information about the complex environment and changing sets of collaboration partners internally. This requirement is in contrast to the usually assumed static structures for embedded systems. In this paper, we present a model-driven approach which overcomes this situation by supporting dynamic data structures while still guaranteeing that valid worst-case execution times can be derived. It supports a flexible resource management which avoids to operate with the prohibitive coarse worst-case boundaries but instead supports to run applications in different profiles which guarantee different resource requirements and put unused resources in a profile at other applications' disposal. By supporting the proper estimation of worst case execution time (WCET) and worst case number of iteration (WCNI) at runtime, we can further support to create new profiles, add or remove them at runtime in order to minimize the over-approximation of the resource consumption resulting from the dynamic data structures required for the outlined class of advanced systems.