Linear Stochastic Fluid Networks

We introduce open stochastic fluid networks that can be regarded as continuous analogs or fluid limits of open networks of infinite-server queues. Random exogenous input may come to any of the queues. At each queue, a cdf-valued stochastic process governs the proportion of the input processed by a given time after arrival. The routing may be deterministic (a specified sequence of successive queue visits) or proportional, i.e., a stochastic transition matrix may govern the proportion of output routed from one queue to another. This stochastic fluid network with deterministic cdf’s governing processing at the queues arises as the limit of normalized networks of infinite-server queues with batch arrival processes where the batch sizes grow. In this limit, one can think of each particle having an evolution through the network, depending on its time and place of arrival, but otherwise independent of all other particles. A key property associated with this independence is the linearity: The workload associated with a superposition of inputs, each possibly having its own pattern of flow through the network, is simply the sum of the component workloads. Just like infinite-server queueing models, the tractability makes the linear stochastic fluid network a natural candidate for approximations.