This paper presents an upper bound on the carried traffic of connections (or equivalently, a lower bound on the blocking probability) for any routing and wavelength assignment (RWA) algorithm in a reconfigurable optical network using wavelength division multiplexing. The bound scales with the number of wavelengths and is achieved asymptotically by a fixed RWA algorithm. The bound can be used as a metric to compare the performance of different RWA algorithms for networks of moderate size. The paper also derives a similar bound for optical networks using dynamic wavelength converters and compares the two cases. It quantifies the amount of wavelength reuse achievable in large networks using the SP-RWA algorithm as a function of the number of wavelengths, number of edges, and number of nodes for randomly constructed networks as well as deBruijn networks. The results show that it is feasible to provide several all-optical connections to each node in a large network using a limited number of wavelengths. For instance, using 32 wavelengths, it is possible to provide 10 full-duplex connections to each node in a 128-node random network with average degree 4, and 5 full-duplex connections per node in a 1000-node random network with average degree 4. The results also show that wavelength converters offer a 10–40% increase in the amount of reuse achievable for our sampling of 14 networks ranging from 16 to 1000 nodes when the number of wavelengths available is small (10 or 32). The paper also discusses the implications of the results and concludes that the bound can be used as a benchmark to compare the performance of different RWA algorithms.This paper presents an upper bound on the carried traffic of connections (or equivalently, a lower bound on the blocking probability) for any routing and wavelength assignment (RWA) algorithm in a reconfigurable optical network using wavelength division multiplexing. The bound scales with the number of wavelengths and is achieved asymptotically by a fixed RWA algorithm. The bound can be used as a metric to compare the performance of different RWA algorithms for networks of moderate size. The paper also derives a similar bound for optical networks using dynamic wavelength converters and compares the two cases. It quantifies the amount of wavelength reuse achievable in large networks using the SP-RWA algorithm as a function of the number of wavelengths, number of edges, and number of nodes for randomly constructed networks as well as deBruijn networks. The results show that it is feasible to provide several all-optical connections to each node in a large network using a limited number of wavelengths. For instance, using 32 wavelengths, it is possible to provide 10 full-duplex connections to each node in a 128-node random network with average degree 4, and 5 full-duplex connections per node in a 1000-node random network with average degree 4. The results also show that wavelength converters offer a 10–40% increase in the amount of reuse achievable for our sampling of 14 networks ranging from 16 to 1000 nodes when the number of wavelengths available is small (10 or 32). The paper also discusses the implications of the results and concludes that the bound can be used as a benchmark to compare the performance of different RWA algorithms.