Reaching Optimum Solutions for the Low Power Hard Real-Time Task Allocation on Multiple Heterogeneous Processors Problem

Abstract

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, the power wall is still a barrier to improving the processor design process due to the power consumption of components. Hard real-time systems are part of life critical environments and reducing the energy consumption on such systems is an onerous and complex process. This paper assesses the problem of finding optimum allocations and frequency assignments of hard real-time tasks among heterogeneous processors targeting low power consumption but taking into account timing constraints. We also propose models based on a well-established formulation in the operational research literature of the Multilevel Generalized Assignment Problem (MGAP). We tackle the problem from the perspective of different integer programming mathematical formulations and their interplay on the search for optimal solutions for RM and EDF. Computational experiments show that providing upper bounds determined by a meta-heuristic based on genetic algorithm reduces the time to finding optimal solution from hours to milliseconds, enabling us to still pursue optimum in larger instances.