3 research outputs found
Optimizing Logical Execution Time Model for Both Determinism and Low Latency
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