On the scalability of network management information for inter-domain light-path assessment

We investigate the necessary amount of network management information for light-path assessment to dynamically set up end-to-end light-paths across administrative domains in optical networks. Our focus is on the scalability of partial management information. We pose light-path assessment as a decision problem, and define the performance as the Bayes probability of an erroneous decision. We then characterize the scalability of management information as its growth rate with respect to the total resources of the network to achieve a desired performance. Scalability is achieved if the management information needed is only a negligible fraction of the total network resources. Specifically, we consider in this work one type of partial information that grows only logarithmically with the number of wavelengths supported per link. We derive an upper bound for the Bayes error in terms of the blocking probability when a new call is presented at the entrance of the network. We evaluate the upper bound using both independent and dependent models of wavelength usage for intra- and inter-domain calls. Our study shows that there exists a "threshold effect": The Bayes error decreases to zero exponentially with respect to the load when the load is either below or above a threshold value; and is nonnegligible when the load is in a small duration around the threshold. This suggests that the partial information considered can indeed provide the desired performance, and a small percentage of erroneous decisions can be traded off to achieve a large saving in the amount of management information.