Multiple-Constraint-Aware RWA Algorithms Based on a Comprehensive Evaluation Model:Use in Wavelength-Switched Optical Networks

Because of explosive growth in Internet traffic and high complexity of heterogeneous networks, improving the routing and wavelength assignment （RWA） algorithm in underlying optical networks has become very important. Where there are multiple links between different the node pairs, a traditional wavelength-assignment algorithm may be invalid for a wavelength-switched optical networks （WSON） that has directional blocking constraints. Also, impairments in network nodes and subsequent degradation of optical signals may cause modulation failure in the optical network. In this paper, we propose an RWA algorithm based on a novel evaluation model for a WSQN that has multiple constraints. The algorithm includes comprehensive evaluation model （CEM） and directional blocking constraint RWA based on CEM （DB-RWA）. Diverse constraints are abstracted into various constraint conditions in order to better assign routing and wavelength. We propose using the novel CEM to optimize routing according to an assessed value of constraints on transmission performance. This eliminates the effects of physical transmission impairments in a WSON. DB-RWA based on CEM abstracts directional blocking conditions in multiple links between network nodes into directional blocking constraints. It also satisfies rigorous network specifications and provides flexibility, scalability, and first-fit rate for the backbone, especially in multiple links between WSON nodes.

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests＇ relationship, we not only consider users＇ attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model （I-FGM） to incorporate these factors. Experiments on two data sets （bookmarking and music network） demonstrate that this predictive method can achieve better results than the other three methods （ANN, NB, and SVM）.