Energy-Aware Scheduling for Aperiodic Tasks on Multi-core Processors

As the performance of modern multi-core processors increases, the energy consumption in these systems also increases significantly. Dynamic Voltage and Frequency Scaling (DVFS) is considered an efficient scheme for achieving the goal of saving energy. In this paper, we consider scheduling a set of independent aperiodic tasks, whose release times, deadlines and execution requirements are arbitrarily given, on DVFS-enabled multi-core processors. Our goal is to meet the execution requirements of all the tasks, and to minimize the overall energy consumption on the processor. Instead of seeking optimal solutions with high complexity, we aim to design lightweight algorithms suitable for real-time systems, with good performances. By applying a subinterval-based method, we come up with a simple algorithm to allocate tasks' available execution times during a heavily overlapped subinterval based on their desired execution requirement during that subinterval. Based on the allocated available execution times, we further consider the final frequency setting and task scheduling, which guarantee that all tasks meet their execution requirements, and tries to minimize the overall energy consumption. Extensive simulations for various platform and task characteristics and evaluations using a practical processor's power configuration indicate that our proposed algorithm has a good performance in terms of saving processor energy, though it has low complexity. Besides, the proposed algorithm is easy to be implemented in practical systems.