Dynamic establishment of differentiated survivable lightpaths in WDM mesh networks

In the emerging next-generation transport networks, called intelligent optical transport networks, DWDM-based optical components like add-drop multiplexers and optical cross connects will have full knowledge of the wavelengths in the network, status, and traffic carrying capacity of each wavelength. With such intelligence, these intelligent optical networks could create self-connecting and self-regulating connections on-the-fly. Because these networks carry huge volume of traffic, maintaining a high level of service availability at an acceptable level of overhead is an important and critical issue. It is essential to incorporate fault-tolerance into QoS requirements. Since the delay (response time) becomes unbounded as the load approaches the link capacity, delay is usually dominated by the most heavily loaded links; therefore algorithms must attempt to minimize the delay. In this paper, we take fault-tolerance, delay, bandwidth, and availability as QoS parameters to provide varying classes of services based on the type of primary and backup lightpaths and the number of backup lightpaths for studying the problem of dynamically establishing lightpaths in WDM optical networks. The type of primary and backup lightpaths determines the QoS parameters such as response time and bandwidth. Whereas, the number of backup lightpaths determines the level of faulttolerance and availability of the connection. Based on the service classes, any connection in the network falls into one of the seven classes, viz. single dedicated primary and single dedicated backup, single dedicated primary and multiple dedicated backups, single dedicated primary and single shared backup, single shared primary and single shared backup, single shared primary and multiple shared backup, single dedicated multi-hop primary and single dedicated multi-hop backup, and single shared multi-hop primary and single shared multi-hop backup. We conduct extensive simulation experiments to compare and evaluate the effectiveness of different classes for varying network configurations—varying number of fibers, wavelengths on physical links, and different traffic patterns. q 2003 Elsevier B.V. All rights reserved.