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，任务计算始终占用固定时长（称为任务的LET区间），任务在区间起始时刻执行读取操作，在区间结束时刻执行写入操作。与其他通信机制（如隐式通信和动态缓冲协议DBP）相比，LET在端到端延迟（包括响应时间和数据时效性）及时间偏移抖动等多个指标上表现不佳。相较于标准LET配置，灵活LET（fLET）模型通过引入虚拟偏移量推迟读取操作、设置虚拟截止时间提前写入操作，在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，具有显著改善端到端时序性能的潜力。为充分释放fLET的潜力，本文研究了其虚拟偏移量与虚拟截止时间的优化分配问题。我们提出了描述任务通信模式的新抽象机制，以及高效探索解空间的新型优化算法。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并提供理论保证。所提出的框架支持多性能指标优化，并在端到端延迟优化中保证有界次优性。实验结果表明，相较于标准LET及其现有扩展方案，我们的优化算法在端到端延迟、时间偏移及其抖动等多项指标上均取得显著提升，并大幅优于隐式通信与动态缓冲协议。