Towards optimal solutions for the low power hard real-time task allocation on multiple heterogeneous processors

The usage of heterogeneous multicore platforms is appealing for applications, e.g. hard real-time systems, due to the potential reduced energy consumption offered by such platforms. However, even in such platforms the power wall phenomena still imposes limits to performance. Hard real-time systems are part of life critical environments and reducing the energy consumption on such systems is an onerous and complex process. We tackle the problem from the perspective of different representative integer programming mathematical formulations and their interplay on the search for optimal solutions for Rate Monotonic (RM) and Earliest Deadline First (EDF) scheduling algorithms. The proposed models are based on a well-established formulation in the operational research literature, namely, the Multilevel Generalized Assignment Problem (MGAP). This paper, therefore, assesses the problem of finding optimal allocations and frequency assignments of hard real-time tasks among heterogeneous processors targeting low power consumption, but taking into account timing constraints. Computational experiments show that finding optimal solutions reduces the estimated energy consumption of the evaluated cases when compared to state-of-the-art algorithms.