Many real-time embedded systems process event streams that are composed of a finite number of different event types. Each different event type on the stream would typically impose a different workload to the system, and thus the knowledge of possible correlations and dependencies between the different event types could be exploited to get tighter analytic performance bounds of the complete system. We propose an abstract stream model to characterize such an event stream. The model captures the needed information of all possible traces of a class of event streams. Hence, it can be used to obtain hard bounded worst-case and best-case analysis results of a system. We show how the proposed abstract stream model can be obtained from a concrete stream specification, and how it can be used for performance analysis. The applicability of our approach and its advantages over traditional worst-case performance analysis are shown in a case study of a multimedia application.
Abstract. Many real-time embedded systems process event streams that are composed of a finite number of different event types. Each different event type on the stream would typically impose a different workload to the system, and thus the knowledge of possible correlations and dependencies between the different event types could be exploited to get tighter analytic performance bounds of the complete system. We propose an abstract stream model to characterize such an event stream. The model captures the needed information of all possible traces of a class of event streams. Hence, it can be used to obtain hard bounded worst-case and best-case analysis results of a system. We show how the proposed abstract stream model can be obtained from a concrete stream specification, and how it can be used for performance analysis. The applicability of our approach and its advantages over traditional worst-case performance analysis are shown in a case study of a multimedia application.
Abstract-While mapping a streaming (such as multimedia or network packet processing) application onto a specified architecture, an important issue is to determine the input stream rates that can be supported by the architecture for any given mapping. This is subject to typical constraints such as on-chip buffers should not overflow, and specified playout buffers (which feed audio or video devices) should not underflow, so that the quality of the audio/video output is maintained. The main difficulty in this problem arises from the high variability in execution times of stream processing algorithms, coupled with the bursty nature of the streams to be processed. In this paper we present a mathematical framework for such a rate analysis for streaming applications, and illustrate its feasibility through a detailed case study of a MPEG-2 decoder application. When integrated into a tool for automated design-space exploration, such an analysis can be used for fast performance evaluation of different stream processing architectures.
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