Energy efficient semi-partitioned scheduling for embedded multiprocessor streaming systems

In this paper, we study the problem of energy minimization when mapping streaming applications with throughput constraints to homogeneous multiprocessor systems in which voltage and frequency scaling is supported with a discrete set of operating voltage/frequency modes. We propose a soft real-time semi-partitioned scheduling algorithm which allows an even distribution of the utilization of tasks among the available processors. In turn, this enables processors to run at a lower frequency, which yields to lower energy consumption. We show on a set of real-life applications that our semi-partitioned scheduling approach achieves significant energy savings compared to a purely partitioned scheduling approach and an existing semi-partitioned one, EDF-os, on average by 36 % (and up to 64 %) when using the lowest frequency which guarantees schedulability and is supported by the system. By using a periodic frequency switching scheme that preserves schedulability, instead of this lowest supported fixed frequency, we obtain an additional energy saving up to 18 %. Although the throughput of applications is unchanged by the proposed semi-partitioned approach, the mentioned energy savings come at the cost of increased memory requirements and latency of applications.