Optimizing utilization in logical execution time system with preserved externally-observable timed I/O semantics

Complex real-time functions, such as autonomous driving, are modeled as directed acyclic graphs (DAGs) of tasks that communicate with each other, subject to strict end-to-end latency constraints. The Logical Execution Time (LET) task model supports verification of these constraints by providing predictable and composable timed input/output (I/O). LET-based effect chains produce deterministic external behavior, defined through Externally-observable Timed I/O Semantics (ETIOS), where the external output traces are uniquely determined by the external input traces. However, these chains may include ineffective computations that do not impact the external output. Analyzing the effectiveness of these computations is further complicated by whether tasks are stateful (maintaining internal state) or stateless (not maintaining internal state). This paper proposes an offline optimization approach to achieve ETIOS withimproved utilization (computational demand) by deriving multiframe tasks that include only effective computations under loosened constraints. Our method effectively manages DAGs that combine both stateful and stateless tasks. A key contribution of our approach is the explicit modeling and analysis of how internal state influences data propagation within LET-based effect chains. Experiments on synthetic DAGs show utilization reductions of up to 77.79%, maximal for pure-stateless DAGs with unit max in-/out-degree and large period differences.