Predictive Outage Intelligence
Noisy Atom is collaborating with Cisco to deliver an autonomous, self-learning network monitoring agent. By processing massive streams of real-time telemetry (CPU load, latency, packet loss, availability) and correlating them with historical alarm/incident databases, the platform builds high-accuracy predictions of impending system outages.
Project Metrics
Under the Hood: Network Asset Modeling
Technical Deep Dive
Ingestion & Preprocessing
Real-time telemetry streams from routers and switches represent continuous time-series data. In contrast, historical incident reports and alarms are asynchronous discrete events. Noisy Atom implemented a sliding-window preprocessor. Telemetry events are aligned on a common epoch grid, converting unstructured incidents into dense feature vectors containing alert counts, severity levels, and gradient trends over a 15-minute history block.
Continuous Learning Loop
A primary challenge of network environments is drift: models trained on campus configurations fail on data center topologies. Our solution leverages Cisco's sandboxed testing clusters. Active network events continuously feed a local loss evaluation node. If a newly adjusted model outperforms the baseline production model on validation assets over a 24-hour cycle, the server swaps weight matrices seamlessly without dropping active prediction requests.
Telemetry & Log Ingestion Pipeline
Telemetry streams are aggregated from distributed network assets, queued dynamically via Apache Kafka, and processed through a Celery worker pool to extract clean sequence events.
Recurrent LSTM Neural Network
Standard feed-forward neural networks struggle with temporal relationships (e.g. alert A occurring 15-20 minutes before failure B). To address this, Noisy Atom implemented an LSTM (Long Short-Term Memory) network architecture.
An LSTM maintains an internal memory state C_t that carries critical historical dependencies over time. Using forget, input, and output gates, the neural network learns which incoming telemetry spikes represent temporary noise and which ones denote warning signs of an impending asset outage.
Self-Learning Feedback Loop
When outages occur, the actual event is correlated with the system's previous prediction. If the model predicted a low probability, the backpropagation loss is used to adjust weights. The network continuously updates its models, prioritizing configurations with higher precision. Models 'learn' from a live customers network, meaning the model is trained on that specific customers network and can be fine tuned for higher accuracy.