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Hierarchical source routing through clouds

Based on a loss network model, we present an adaptive source routing scheme for a large, hierarchically-organized network. To represent the "available" capacity of a cloud (subnetwork), we compute the average implied cost to go through or into the cloud. Such implied costs reflect the congestion in the cloud as well as the interdependencies among traffic streams in the network. We prove that both a synchronous and asynchronous distributed computation of the implied costs will converge to a unique solution under a light load condition. To assess accuracy, we derive a bound on the difference between our implied costs and those calculated for a flat network. In addition, we show how on-line measurements can be incorporated into the routing algorithm, and we present some representative computational results which demonstrate the ability of our scheme to appropriately route high level flows while significantly reducing complexity.