Dynamic task graph scheduling on multicore processors for performance, energy, and temperature optimization

Despite significant advancements in multicore processor technology for reducing the chip-level energy consumption, higher levels of power dissipation resulting in thermal implications and cooling costs remain as unsolved problems. Although several scheduling methods of controlling and managing the power dissipation and temperature exist, most schemes are static that are unable to adjust to the dynamic program and system changes. This paper presents dynamic method for voltage-scaling based task scheduling for simultaneous optimization of performance, energy, and temperature (PET quantities) under dynamically varying task and system conditions. Our method generates an initial set of Pareto optimal solutions utilizing a multi-objective evolutionary algorithm (MOEA) called SPEA-II (Strength Pareto Evolutionary Algorithm). This set of solutions is dynamically evolved with time to minimize the deviation of PET quantities from the Pareto optimal values. We carried out extensive evaluations using several task graph benchmarks based on the data obtained from a real multicore machine. The results indicate that the proposed dynamic re-optimization achieves up to 8% improvement in PET quantities as compared to the statically selected schedule.