Second Derivative Algorithms for Minimum Delay Distributed Routing in Networks

We propose a class of algorithms for finding an optimal quasi-static routing in a communication network. The algorithms are based on Gallager´s method [1] and provide methods for iteratively updating the routing table entries of each node in a manner that guarantees convergence to a minimum delay routing. Their main feature is that they utilize second derivatives of the objective function and may be viewed as approximations to a constrained version of Newton´s method. The use of second derivatives results in improved speed of convergence and automatic stepsize scaling with respect to level of traffic input. These advantages are of crucial importance for the practical implementation of the algorithm using distributed computation in an environment where input traffic statistics gradually change.