A Survey on Schedulability Analysis of Rate-Adaptive Tasks
Published in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019
Recommended citation: P. G. Shambharkar, S. Bhambri, A. Goel and M. N. Doja, "A Survey on Schedulability Analysis of Rate-Adaptive Tasks," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019, pp. 277-282, doi: 10.1109/COMITCon.2019.8862266. https://ieeexplore.ieee.org/abstract/document/8862266
Abstract: In automotive real-time systems applications, Multiprocessor Control Units (MCU) handle the engine particular tasks which depend on the specific crankshaft rotation angles and are also a function of the instantaneous angular velocity of the engine. The timing behavior of Adaptive Variable Rate (AVR) tasks has been analyzed in several papers, whose results allow verifying the behavior of engine control applications under different sets of assumptions. This survey paper highlights the major scheduling policies that have been studied in past. In particular, scheduling of such tasks at an arbitrary mode is derived for static as well as dynamic state of the engine.