Inferring energy and performance cost of RTOS in priority-driven scheduling

We present a high-level method for rapidly and accurately estimating energy and performance cost of Real-Time Operating Systems. We investigate priority-driven scheduling and assume inter-dependent tasks competing for shared resources. Unlike most other approaches, which rely on Transaction-Level Modeling (TLM), we infer the information we need directly from executing the algorithmic specification, without needing to build any high-level architectural model. We distinguish two main components in our approach: first, an accurate one-time pre-characterization of the main RTOS functionalities in terms of energy and cycles; second, the development of an algorithm to rapidly predict the occurrences of such RTOS functionalities. Finally, we validate our approach by comparing it against gate level for accuracy and against TLM for speed. We obtain a worst-case error of 12% against a mean speedup of ~30X.