Iterative approach to optimizing convergence routing priorities

This paper shows how to optimize the routing decisions in a nondeterministic routing algorithm called convergence routing in which routes may change depending on the traffic conditions. The routing algorithm guarantees a loss-free delivery of data packets from bursty sources, and a deterministic bound on the route length in arbitrary topology networks. The routing decisions are based on assigning routing priorities to the links such that a packet is forwarded to the highest priority link which is not blocked. Routing priorities are assigned using a local-greedy metric which minimizes the distance (number of hops) to the destination. This work shows that routing decisions using a local-greedy metric are not optimal, and the performance of the algorithm can be improved substantially by using new measures. Thus, various look-ahead metrics which take into account the potential gain on the other switching nodes toward the destination of a packet are suggested. The contributions of this work are: (1) a new analytical model to capture the behavior of a switching node; (2) an iterative optimization technique to set routing priorities according to various look-ahead measures; and (3) heuristics to ensure the stability of the routing priorities. The optimization objective is to maximize the throughput by minimizing the maximum total flow carried on a link in the network under static traffic model. The performance is studied computationally on various networks and traffic matrices. It is shown that up to a 50% performance increase can be obtained by optimizing the routing priorities.