The Logical Execution Time (LET) programming model has recently received considerable attention, particularly because of its timing and dataflow determinism. In LET, task computation appears always to take the same amount of time (called the task's LET interval), and the task reads (resp. writes) at the beginning (resp. end) of the interval. Compared to other communication mechanisms, such as implicit communication and Dynamic Buffer Protocol (DBP), LET performs worse on many metrics, such as end-to-end latency (including reaction time and data age) and time disparity jitter. Compared with the default LET setting, the flexible LET (fLET) model shrinks the LET interval while still guaranteeing schedulability by introducing the virtual offset to defer the read operation and using the virtual deadline to move up the write operation. Therefore, fLET has the potential to significantly improve the end-to-end timing performance while keeping the benefits of deterministic behavior on timing and dataflow. To fully realize the potential of fLET, we consider the problem of optimizing the assignments of its virtual offsets and deadlines. We propose new abstractions to describe the task communication pattern and new optimization algorithms to explore the solution space efficiently. The algorithms leverage the linearizability of communication patterns and utilize symbolic operations to achieve efficient optimization while providing a theoretical guarantee. The framework supports optimizing multiple performance metrics and guarantees bounded suboptimality when optimizing end-to-end latency. Experimental results show that our optimization algorithms improve upon the default LET and its existing extensions and significantly outperform implicit communication and DBP in terms of various metrics, such as end-to-end latency, time disparity, and its jitter.

翻译：逻辑执行时间（LET）编程模型因其在时序和数据流方面的确定性而受到广泛关注。在LET模型中，任务计算始终表现为消耗相同的时间（即任务的LET区间），且任务在该区间的起点读取数据，在终点写入数据。与其他通信机制（如隐式通信和动态缓冲区协议DBP）相比，LET在端到端延迟（包括反应时间和数据年龄）及时间差异抖动等多个指标上表现欠佳。相较于默认的LET设置，灵活LET（fLET）模型通过引入虚拟偏移量推迟读取操作、利用虚拟截止时间提前写入操作，在仍能保证可调度性的前提下缩短LET区间。因此，fLET在保持时序与数据流确定性优势的同时，具备显著提升端到端时序性能的潜力。为充分实现fLET的潜力，我们研究了其虚拟偏移量与虚拟截止时间的优化分配问题。我们提出描述任务通信模式的新型抽象机制，并设计优化算法以高效探索解空间。该算法利用通信模式的线性化特性，通过符号化操作实现高效优化，同时提供理论保证。该框架支持多性能指标优化，并在优化端到端延迟时保证有界次优性。实验结果表明，我们的优化算法相较于默认LET及其现有扩展方案均有提升，且在端到端延迟、时间差异及其抖动等多个指标上显著优于隐式通信和DBP。