When faults occur in optical networks, many derivative alarms are generated from the root alarms, because of the derived properties of optical network alarms. In a large and complex optical network, the management system will receive tremendous alarms even though there is only one actual fault. Therefore, accurate fault localization has significance for network operation and maintenance. The complex relationship between alarms and faults makes it difficult to locate faults, especially multiple faults. To solve this problem, this paper proposes a fault localization method based on knowledge graph and network topology information. Using the information association ability of the knowledge graph, the implicit relationship between the alarm data is mined, and the graph neural network is used to perform automatic fault reasoning on the alarm knowledge graph. Further, the network topology information is integrated into the fault localization, which increases the dimension of the knowledge graph. Simulation results show that the proposed method can achieve 97% accuracy in multiple fault localization scenarios.
In optical networks, fast and accurate fault localization ensures the normal operation and reliable transmission of a large number of network services, which has an important research significance. With the growing scale and complexity of optical networks, optical network fault localization becomes more challenging. Using the effective feature extraction capability of deep learning, this paper proposes a fault localization method based on deep learning, which improves the performance of fault localization in optical networks. First, the principle of deep learning and how it is applied to fault localization tasks are analyzed. Furthermore, the data is preprocessed to meet the input requirements of the deep learning model. Finally, using the preprocessed data set to train and verify the deep learning models with different parameters, and determine the optimal model parameters. The simulation results show that compared with the existing fault localization algorithms, the duration of the proposed fault localization method is shorter, the fault localization accuracy is higher, the fault localization delay is between 0.30 and 0.40ms, and the accuracy rate reaches more than 95%.
The performance of LEO satellite optical networks varies with the satellite constellation. For simulating the performance of highly dynamic LEO satellite optical networks in different constellations quickly, this paper designs a constellation programmable large-scale LEO satellite optical network platform. The platform takes the parameters of the constellation and the change of inter-satellite link and ground-to-satellite link as input to build the dynamic network topology. For reducing the redundant storage of the link-state, the paper proposes a novel satellite network topology representation data structure, which combines the initial topology and the circular mapping linked list, to dynamically build topology that change over time. Based on the platform, we have conducted the performance evaluation of different constellations with different numbers of ground-to-satellite links. The simulation results show that the inclined orbit constellation cannot cover the polar regions of high latitudes, while in the area that can be covered by the constellation, there are more ground-to-satellite links in inclined orbit constellation.
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