Buffer allocation for real-time streaming applications running on heterogeneous multi-processors without back-pressure

The goal of buffer allocation for real-time streaming applications is to minimize total memory consumption, while reserving sufficient space for each data production, without overwriting any live data and guaranteeing the satisfaction of real-time constraints. Previous research has mostly focused on buffer allocation for systems with back-pressure. This paper addresses 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.Our contributions are (1) extension of the available dataflow techniques with best-case analysis; (2) the closest common dominator-based and closest common predecessor-based lifetime analysis techniques; (3) techniques to model the initialization behavior and enable token reuse.Our benchmark set includes an MP3 decoder, a WLAN receiver, an LTE receiver and an LTE-Advanced receiver. We consider two key features of LTE-Advanced: (1) carrier aggregation and (2) EPDCCH processing. Through our experiments, we demonstrate that our techniques are effective in handling the complexities of real-world applications. For the LTE-Advanced receiver case study, our techniques enable us to compare buffer allocation required for different scheduling policies with effective impact on architectural decisions. A key insight in this comparison is that our improved techniques show a different scheduling policy to be superior in terms of buffer sizes compared to our previous technique. This dramatically changes the trade-off among different scheduling policies for LTE-Advanced receiver