Automotive architectures consist of multiple electronic control units (ECUs) which run distributed control applications. Such ECUs are connected to sensors and actuators and communicate via shared buses. Resource arbitration at the ECUs and also in the communication medium, coupled with variabilities in execution requirements of tasks results in jitter in the signal/data streams existing in the system. As a result, buffers are required at the ECUs and bus controllers. However, these buffers often implement different semantics – FIFO queuing, which is the most straightforward buffering scheme, and data refreshing, where stale data is overwritten by freshly sampled data. Traditional timing and schedulability analysis that are used to compute, e.g., end-to-end delays, in such automotive architectures can only model FIFO buffering. As a result, they return pessimistic delay and resource estimates because in reality overwritten data items do not get processed by the system. In this paper we propose an analytical framework for accurately modeling such data refresh semantics. Our model exploits a novel feedback control mechanism and is purely functional in nature. As a result, it is scalable and does not involve any explicit state modeling. Using this model we can estimate various timing and performance metrics for automotive ECU networks consisting of buffers implementing different data handling semantics. We illustrate the utility of this model through three case studies from the automotive electronics domain.