Abstract

This paper presents a design synthesis method for distributed embedded systems. In such systems, computations can ow through long pipelines of interacting software components, hosted on a variety of resources, each of which is managed byalocal scheduler. Our method automatically calibrates the local resource schedulers to achieve the system's global end-to-end performance requirements. A system is modeled as a set of distributed task chains (or pipelines), where eachtaskrepresents an activity requiring nonzero load from some CPU or network resource. Task load requirements can vary stochastically, due to second-order e ects like cache memory behavior, DMA interference, pipeline stalls, bus arbitration delays, transient head-of-line blocking, etc. We aggregate these e ects { along with a task's per-serviceload demand { and model them via a single random variable, ranging over an arbitrary discrete probability distribution. Load models can be obtained viapro ling tasks in isolation, or simply by using an engineer's hypothesis about the system's projected behavior. The end-to-end performance requirements are posited in terms of throughput and delay constraints. Speci cally, a pipeline's delay constraint is an upper bound on the total latency a computatation can accumulate, from input to output. The corresponding throughput constraint mandates the pipeline's minimum acceptable output rate { counting only outputs which meet their delay constraints