Buffer allocation for dynamic real-time streaming applications running on a multi-processor without back-pressure

Abstract

Buffer allocation for real-time streaming applications, modeled as dataflow graphs, minimizes the total memory consumption while reserving sufficient space for each data production without overwriting any live data and guaranteeing the satisfaction of real-time constraints. We focus on the problem of buffer allocation for systems without back-pressure. Since systems without back-pressure lack blocking behavior at the side of the producer, buffer allocation requires both best- and worst-case timing analysis. Moreover, the dynamic (data-dependent) behavior in these applications makes buffer allocation challenging from the best- and worst-case- timing analysis perspective. We argue that static dataflow cannot conveniently express the dynamic behavior of these applications, leading to overallocation of memory resources. Mode-controlled Dataflow (MCDF) is a restricted form of dynamic dataflow that allows mode switching at runtime and static analysis of real-time constraints. In this paper, we address the problem of buffer allocation for MCDF graphs scheduled on systems without back-pressure. We consider practically relevant applications that can be modeled in MCDF using recurrent-choice mode sequence that consists of the mode sequences of equal length; it provides tractable analysis. Our contribution is a buffer allocation algorithm that achieves up to 36% reduction in total memory consumption compared to the current state-of-the-art for an LTE and an LTE Advanced receiver use cases