LMD: A local minimum driven and self-organized method to obtain locators

The scalability of routing architectures for large networks is one of the biggest challenges that the Internet faces today. Greedy routing, in which each node is assigned a locator used as a distance metric, recently received increased attention from researchers and is considered as a potential solution for scalable routing. In this paper, we propose LMD - a Local Minimum Driven method to compute the topology-based locator. As opposed to previous work, our algorithm employs a quasigreedy and self-organized embedding method, which outperforms similar decentralized algorithms by up to 20% in success rate. To eliminate the negative effect of the “quasi” greedy property - transfer routes longer than the shortest routes, we introduce a two-stage routing strategy, which combines the greedy routing with source routing. The greedy routing path discovered and compressed in the first stage is then used by the following source-routing stage. Through extensive evaluations, based on synthetic topologies as well as on a snapshot of the real Internet AS topology, we show that LMD guarantees 100% delivery rate on large networks with a very low stretch.