Networked embedded systems are expected to support adaptive streaming audio/video applications with soft real-time constraints. These systems can be designed in a cost efficient manner only if their architecture exploits the "leads" suggested by clever compile time performance estimators. However, performance estimation of networked embedded systems is a non-trivial problem. The computational requirements of such systems show statistical variations that stem from several interacting factors. At the slowest time scale, applications can adapt to network bandwidth by configuring the processing functionality of their task (e.g. compression parameters). Also, there could be significant execution time variations within a task. Thus it is tricky to compute the net processing demand of several such applications on a system architecture, especially if the system schedules these applications using prioritized run-time schedulers. In this paper we describe an analytical tool called AsaP for fast performance estimation of such embedded systems. AsaP builds approximate models of these systems and characterizes the processing load on the system as a stochastic process. The output of AsaP is an exact distribution of the processing delay of each application. This is a powerful result that can be leveraged for efficient design of multimedia networked systems requiring soft real-time guarantees. It is also the first known framework that quantifies the effect of runtime schedulers (FCFS, RM, EDF) on the performance of such systems.
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