A New Energy-Aware Dynamic Task Set Partitioning Algorithm for Soft and Hard Embedded Real-Time Systems

Abstract

Power consumption is a major design concern in current embedded systems. To deal with consumption, many systems apply dynamic voltage scaling (DVS) techniques which dynamically change the system speed depending on the workload characteristics. DVS costs in a multicore system can be reduced by sharing the same DVS regulator among the cores. In this context, to handle energy efficiently, the workload must be properly balanced among the cores. This paper proposes a new heuristic algorithm to balance the workload in an embedded system with a coarse-grain multithreaded multicore processor. This heuristic is aimed at improving the overlapping time between the memory and the processor while keeping balanced core utilizations. To this end, the heuristic dynamically drives the frequency/voltage level to guarantee deadline fulfillment of the hard real-time tasks as well as to achieve a good trade-off between deadline losses and energy savings of the soft real-time tasks. The proposed technique has been evaluated on a model of a contemporary high-end ARM embedded microprocessor executing a set of standard embedded benchmarks. Energy savings depend on the range of frequency/voltage levels that the DVS regulator implements. Experimental results show that with the proposed heuristic, when working with hard real-time tasks, the energy consumption is about 33% the energy dissipated by a system without DVS regulator and balancing heuristic. Moreover, when soft real-time tasks are also considered, the normalized consumption presents values ranging in between 8 and 70% depending on the scheduler aggressiveness.