System-level scheduling of real-time streaming applications using a semi-partitioned approach

Modern multiprocessor streaming systems have hard real-time constraints that must be always met to ensure correct functionality. At the same time, these streaming systems must be designed to use the minimum required amount of resources (such as processors and memory). In order to meet such constraints, using scheduling algorithms from the classical real-time scheduling theory represents an attractive solution approach. These algorithms enable: (1) providing timing guarantees to the applications running on the system, and (2) deriving analytically the minimum number of processors required to schedule the applications. So far, designers in the embedded systems community have focused on global and partitioned scheduling algorithms. However, recently, a new hybrid class of scheduling algorithms has been proposed. In this work, we investigate the applicability of a sub-class of these hybrid algorithms, called semi-partitioned algorithms, to applications modeled as Cyclo-Static Dataflow (CSDF) graphs. The contribution of this paper is two fold. First, we devise an approach that enables semi-partitioned scheduling algorithms, even soft real-time ones, to be applied to CSDF graphs while providing hard real-time guarantees at the input/output interfaces with the external environment. Second, we focus on an existing soft real-time semi-partitioned approach, for which we propose an allocation heuristic, called FFD-SP. The proposed heuristic reduces the minimum number of processors required to schedule the applications compared to a pure partitioned scheduling algorithm, while trying to minimize the buffer size and latency increases incurred by the soft realtime approach.