Addressing the problem that the existing retinal vessel segmentation models lose topological information in 3D feature extraction， leading to branch breakage， poor continuity in 2D segmentation results， and missing cross-modal associations in vascular analysis and disease prediction， a collaborative framework， MA_DNet（Multi-scale topology-Aware Disease Network）， was proposed. The framework consists of an enhanced segmentation model， MA_Net+， and a disease prediction module. Based on MA_Net（Multi-scale topology-Aware Network）， an intermediate feature retraining module was introduced by MA_Net+ to refine vessel structures and reconnect broken branches. Firstly， the GMSF （Gated Multi-Scale Fusion） module was employed to extract multi-scale spatial convolutions and fuse complex branch features， and the ResMamba module was combined to model long-range topological dependencies within vessels， so as to enhance 3D feature representations， thereby suppressing topological breakage in segmentation results effectively. Then， the convolutional layers of 2D module MA_Net+ were used to further optimize the continuity of local vascular structure. Finally， a cascade prediction module was designed， combining morphological parameters with clinical indicators， so as to establish cross-modal associations between image features and Coronary Artery Disease （CAD） risk. Experimental results show that the MA_Net+ framework achieves a Dice score of 93.02% and a Jaccard index of 87.04% on one subset of the OCTA-500 public dataset， with improvements of 0.28 and 0.37 percentage points， respectively， compared to the IPN-V2+（Image Projection Network V2+） model； on another OCTA-500 subset， the MA_Net+ framework achieves the two indicators of 89.76% and 81.52%， respectively， with gains of 0.35 and 0.57 percentage points， respectively； the disease prediction module of the MA_Net+ framework achieves an AUC（Area Under Curve） of 86.23% on a private dataset. It can be seen that MA_DNet framework enhances the continuity of vascular segmentation effectively through 3D topological modeling and multi-scale fusion mechanism； meanwhile， the framework explores cross-modal correlation prediction between retinal images and CAD risks， offering a new solution for non-invasive cardiovascular diagnosis.

The key issue of network virtualization is Virtual Network Embedding (VNE), and the rapid growth of energy cost makes infrastructure providers concern energy conservation. An energy conservation VNE algorithm that centrally used network topology for saving energy on VNE problem was presented. The importance of the nodes was characterized by the conception of closeness centrality and the capabilities of the nodes, and the working nodes were preferentially used for resources integration to reduce energy consumption and calculation cost, that ensured the distance of the substrate links won't be too long. The simulation results show that the proposed algorithm improves revenue-energy ratio more than 20% when accept ratio reaches 70% and revenue cost ratio reaches 75%, and has advantages compared with the previous algorithms.