Cross-timestep Fault Prediction with Imbalanced Data for Optical Modules in Internet Data Centers

Optical module faults are among the most serious threats to Internet Data Centers (IDCs), which are crucial to a company's data processing and information storage operations. Consequently, enterprises typically aim to precisely identify faulty optical modules within an extended preparation time, a process known as cross-timestep fault prediction. However, achieving this goal encounters several challenges, including insufficient effective data, long-distance dependency issues, and the problem of data imbalance. In this paper, we propose CTFP, a novel cross-timestep fault prediction method for optical modules in IDCs, which can accurately predict optical module faults twenty-four time steps in advance based on historical data. CTFP not only leverages the Digital Diagnostic Monitoring (DDM) data of optical modules as input, but also considers port-related data that may be affected by optical module faults. In addition, we have incorporated the attention mechanism into CTFP to capture the long-term trends of historical data, thereby mitigating the issue of long-distance dependency. Moreover, we designed an improved loss function that addresses the issue of data imbalance. Finally, we evaluate CTFP on industrial datasets collected from real-world internet data centers. The experimental results demonstrate that, compared to the state-of-the-art methods Bi-GRU and LSTM, CTFP increases the recall by at least 11% and 9% under various sample proportions. Notably, CTFP maintained a remarkably low maximum false positive rate of only 0.26%. In real-world conditions, where the number of optical modules often reaches the hundreds of thousands, maintaining a low false positive rate is imperative.