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Estimation-based profiling for code placement optimization in sensor network programs

In this work, we focus on applying profiling guided code placement to programs running on resource-constrained sensor motes. Specifically, we model the execution of sensor network programs under nondeterministic inputs as discrete-time Markov processes, and propose a novel approach named Code Tomography to estimate parameters of the Markov models that reflect sensor network programs' dynamic execution behavior by only using end-to-end timing information measured at start and end points of each procedure. The parameters estimated by Code Tomography are fed back to compilers to optimize the code placement so that branch misprediction rate can be reduced.