Real-time network routing (RTNR) is a new adaptive routing method. With RTNR, switches have a simple way of exchanging link status bit map information, thereby determining the availability and load conditions of the direct and all two-link paths to the destination. Link busy-idle status is exchanged between the network nodes using a bit map data exchange through the common channel signaling (CCS) network, and calls are set up where there is the most available capacity in the network. To date the analysis of RTNR networks has been limited to simulation models. The present authors develop an analytical model for the AT&T network under RTNR, which is shown to provide good agreement with simulation models. The analytical model for RTNR networks uses an Erlang fixed point method to solve the nonlinear equations describing dynamical network behavior. The equations include the link state probability, network flows, link arrival rates, adaptive trunk reservation level, and adaptive path selection depth. The link state model provides the aggregate link state probabilities through solution of the birth-death equations, and models the adaptive nature of trunk reservation. The network flow model provides a method to calculate the traffic flow using the least busy concept employed in RTNR, and also models the adaptive nature of the path selection depth. The analytical model addresses asymmetrical networks, and computational examples show the differences from the simulation model to be small. The authors also use the analytical model to examine key RTNR parameters over a range of values.
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