Temporal Attention-Guided Sequence Learning for Zero-Shot Generalization in Datacenter Network Failures

As hyperscale datacenter topologies grow increasingly complex, static Z-Score thresholding and classical stateless anomaly algorithms (like Random Forests) struggle to map the chaotic, multi-dimensional signatures preceding catastrophic network outages. Compounding this challenge is the extreme class imbalance inherent in network telemetry: millions of stable communication sequences are generated for every single failure sequence. We present a highly novel, deep learning approach for early-warning preventative alerting. By applying a Long Short-Term Memory (LSTM) Autoencoder infused with a Self-Attention Mechanism natively weighted against soft-failure occurrences, our model dynamically isolates critical cascading latency signatures to predict hard outages up to 60 seconds before they manifest. We demonstrate zero-shot generalization across disparate optical hardware topologies, proving robust cross-validation against single-dataset hardware bias.