Network Monitoring Data Recovery Based on Flexible Bi-Directional Model

Abstract

Comprehensive network monitoring data is crucial for anomaly detection and network optimization tasks. However, due to factors such as sampling strategies and failures in data transmission or storage, only incomplete monitoring data can be obtained. Traditional techniques for completing network monitoring data matrices have limitations in leveraging network-related features and lack the adaptability required for offline and online execution. In this paper, we introduce a novel approach that significantly improves the integration of network features and operational flexibility in data completion tasks. By converting the data matrix into a bipartite graph and integrating network features into the graph's node attributes, we redefine the problem of missing data completion. This transformation reframes the issue as estimating unobserved edges in the bipartite graph. We propose the Bi-directional Bipartite Graph Completion (BGC) model, a flexible framework that seamlessly adapts to both offline and online data completion tasks. This model encapsulates static, dynamic, bi-directional temporal features and network topology, thereby improving the accuracy of unobserved edge estimation. Experiments conducted on two public data traces demonstrate the superiority of our method over six baseline models. Our method not only achieves higher accuracy in offline scenarios but also displays remarkable speed in online situations.