On multicore platforms, reliable task execution, as well as low energy consumption, are essential. Dynamic Voltage/Frequency Scaling (DVFS) is typically used for energy saving, but with a negative impact on reliability, especially when the frequency is low. Using high frequencies to meet reliability constraints is not always feasible, while multiple replicas increase energy consumption. To minimize energy consumption, enhancing reliability, without violating real-time constraints, we propose an approach that combines distinct reliability enhancement techniques, under task-level, processor-level and system-level DVFS. Our task mapping problem jointly optimizes task allocation, task frequency assignment, and task duplication, under multiple constraints. This is achieved by formulating the task mapping problem as a mixed integer non-linear programming problem and equivalently transforming it into a mixed integer linear programming, that is optimally solved. From the obtained results, the proposed approach achieves better energy consumption and finds solutions, when other approaches fail.Acknowledgements This work is founded by China Scholarship Council (CSC).
An efficient task execution on multicore platforms can lead to low energy consumption. To achieve that, an Integer Non-Linear Programming (INLP) formulation is proposed that performs task mapping by jointly addressing task allocation, task frequency assignment, and task duplication. The goal is to minimize energy consumption under real-time and reliability constraints. To provide an optimal solution, the original INLP problem is safely transformed to an equivalent Mixed Integer Linear Programming (MILP) problem. The comparison of the proposed approach with existing energy-aware task mapping approaches shows that the proposed approach is able to find solutions when other approaches fail, achieving an overall lower energy consumption.
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