Unsupervised and Network-Aware Diagnostics for Latent Issues in Network Information Databases
Hua Shao, Li Chen, Youjian Zhao · 2020
Network management database (NID) is essential in modern large-scale networks. Operators rely on NID to provide accurate and up-to-date data, however, NID-like any other databases-can suffers from latent issues such as inconsistent, incorrect, and missing data. In this work, we first reveal latent data issues in NIDs using real traces from a large cloud provider, Tencent. Then we design and implement a diagnostic system, NAuditor, for unsupervised identification of latent issues in NIDs. In the process, we design a compact and graph-based data structure to efficiently encode the complete NID as a Knowledge Graph, and model the diagnostic problems as unsupervised Knowledge Graph Refinement problems. We show that the new encoding achieves superior performance than alternatives, and can facilitate adoption of state-of-the-art KGR algorithms. We also have used NAuditor in a production NID, and found 71 real latent issues, which all have been confirmed by operators.