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The Logical Execution Time (LET) programming model has recently receivedconsiderable attention, particularly because of its timing and dataflowdeterminism. In LET, task computation appears always to take the same amount oftime (called the task's LET interval), and the task reads (resp. writes) at thebeginning (resp. end) of the interval. Compared to other communicationmechanisms, such as implicit communication and Dynamic Buffer Protocol (DBP),LET performs worse on many metrics, such as end-to-end latency (includingreaction time and data age) and time disparity jitter. Compared with thedefault LET setting, the flexible LET (fLET) model shrinks the LET intervalwhile still guaranteeing schedulability by introducing the virtual offset todefer the read operation and using the virtual deadline to move up the writeoperation. Therefore, fLET has the potential to significantly improve theend-to-end timing performance while keeping the benefits of deterministicbehavior on timing and dataflow.  To fully realize the potential of fLET, we consider the problem of optimizingthe assignments of its virtual offsets and deadlines. We propose newabstractions to describe the task communication pattern and new optimizationalgorithms to explore the solution space efficiently. The algorithms leveragethe linearizability of communication patterns and utilize symbolic operationsto achieve efficient optimization while providing a theoretical guarantee. Theframework supports optimizing multiple performance metrics and guaranteesbounded suboptimality when optimizing end-to-end latency. Experimental resultsshow that our optimization algorithms improve upon the default LET and itsexisting extensions and significantly outperform implicit communication and DBPin terms of various metrics, such as end-to-end latency, time disparity, andits jitter.

Optimizing Logical Execution Time Model for Both Determinism and Low Latency