Numerous Directed-Acyclic Graph (DAG) schedulers have been developed to improve the energy efficiency of various multi-core systems. However, the DAG monitoring modules proposed by these schedulers make a priori assumptions about the workload and relationship between the task dependencies. Thus, schedulers are limited to work on a limited subset of DAG models. To address this problem, we propose a unified online DAG monitoring solution independent from the connected scheduler and able to handle all possible DAG models. Our novel low-complexity solution processes online the DAG of the application and provides relevant information about each task that can be used by any scheduler connected to it. Using H.264/AVC video decoding as an illustrative application and multiple configurations of complex synthetic DAGs, we demonstrate that our solution connected to an external simple energy-efficient scheduler is able to achieve significant improvements in energy-efficiency and deadline miss rates compared to existing approaches.
François Maréchal, Daniel Alexander Florez Orrego, Meire Ellen Gorete Ribeiro Domingos, Réginald Germanier
David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi
David Atienza Alonso, Miguel Peon Quiros, José Angel Miranda Calero, Hossein Taji