Nowadays, employing the worst case analysis is the most common approach to provide unified static task mapping–scheduling plans on MPSoCs. Since the whole design space nor a subset of design space are not explored in the worst case methods, these approaches may fail to achieve efficient performance yield. In this paper, we present a temperature-aware quasi-static task mapping–scheduling framework under process variation for hard real-time and periodic systems on MPSoCs. By employing the stochastic optimization and scenario-based approaches, we explore a few representative scenarios in the whole design space of the chip using the probability density function of the problem random variables. Then, we obtain a compact set of near optimal mapping–scheduling of real-time tasks which targets performance-yield maximization and minimization of the expected values of peak temperature. Consequently, considering different chip parameter configurations, we construct the plan set as the solutions that attain the best variation-aware task mapping–scheduling that satisfy the deadline and minimize the temperature. This plan set can readily look up at run time by the system scheduler of the chip to find the proper plan of the tasks based on the run-time parameters. The experimental results demonstrate significant improvements in performance-yield and peak temperature for almost all of the test cases off homogenous and heterogeneous MPSoCs.

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