Unified theory of real-time task scheduling and dynamic voltage/frequency Scaling on MPSoCs

Dynamic voltage/frequency scaling (DVFS) and adaptive body biasing (ABB) have shown to effectively reduce dynamic and leakage energy consumption in real-time embedded systems. Although these techniques exploit the slack time on a given task ordering, the task ordering may not provide a slack time distribution that DVFS/ABB can benefit from and this can limit the potential energy saving such techniques can provide. In this paper, we present an optimal network flow based solution for simultaneous static real-time scheduling and energy minimization (DVFS and ABB) on multiprocessors. Results show that our optimal solution reduces the energy dissipation by 47.84%, 26.21% and 17.46%, on average, in comparison with no-DVFS execution, voltage scaling algorithm with virtual continuous speed and an optimal energy minimization algorithm without task re-ordering, respectively.