Capacity Optimization for Surviving Double-Link Failures in Mesh-Restorable Optical Networks

Abstract

Most research to date in survivable optical network design and operation, focused on the failure of a single component such as a link or a node. A double-link failure model in which any two links in the network may fail in an arbitrary order was proposed recently in literature [1]. Three loop-back methods of recovering from double-link failures were also presented. The basic idea behind these methods is to pre-compute two backup paths for each link on the primary paths and reserve resources on these paths. Compared to protection methods for single-link failure model, the protection methods for double-link failure model require much more spare capacity. Reserving dedicated resources on every backup path at the time of establishing primary path itself would consume excessive resources. Moreover, it may not be possible to allocate dedicated resources on each of two backup paths around each link, due to the wavelength continuous constraint. In M. Sridharan et al., [2,3] we captured the various operational phases in survivable WDM networks as a single integer programming based (ILP) optimization problem. In this work, we extend our optimization framework to include double-link failures. We use the double-link failure recovery methods available in literature, employ backup multiplexing schemes to optimize capacity utilization, and provide 100% protection guarantee for double-link failure recovery. We develop rules to identify scenarios when capacity sharing among interacting demand sets is possible. Our results indicate that for the double-link failure recovery methods, the shared-link protection scheme provides 10–15% savings in capacity utilization over the dedicated link protection scheme which reserves dedicated capacity on two backup paths for each link. We provide a way of adapting the heuristic based double-link failure recovery methods into a mathematical framework, and use techniques to improve wavelength utilization for optimal capacity usage.