An Hybrid Prediction-based Routing approach for reducing routing inaccuracy in optical transport networks

The advent of network technologies such as Automatically Switched Optical Networks (ASON) and Generalized Multiprotocol Label Switching (GMPLS) pave the way to the deployment of flexible optical transport networks (OTNs). The flexibility of OTNs is a feature highly demanded in dynamic scenarios where lightpaths are continuously set up and torn down on a short-term basis. Unfortunately, the availability and accuracy of network state information in dynamic scenarios are both limited, causing a severe impact on both performance and scalability of Routing and Wavelength Assignment (RWA) algorithms. In this paper we devise a promising routing scheme so-called Hybrid Prediction-based Routing (HPBR). HPBR combines prediction strategies with a novel method to select the most suitable routing metric, aiming at reducing both the dissemination of network state information and the blocking probability. Our findings validate that the proposed scheme significantly reduces the blocking probability compared with other routing schemes, while avoiding the need to periodically disseminate network state information.