Advanced Time Series Models for Predictive Modeling and Early Detection of SFP Failures

Predicting failures in network components, such as Small Form-factor Pluggable (SFP) ports in Brocade switches within Storage Area Networks (SANs), is crucial for maintaining network reliability and optimizing performance. A key parameter for assessing SFP health is transmit power (tx-power); when tx-power falls below a threshold of 250 mV, it indicates an impending failure. This paper explores the use of Machine Learning and Deep Learning techniques to forecast the lifespan of SFPs, enabling proactive maintenance and enhancing network reliability. Traditional methods often struggle to capture complex, long-term dependencies within failure data. To address this, we investigate advanced time series models, including Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and Support Vector Regression (SVR), for their effectiveness in capturing temporal dependencies and seasonality. Operational data, including logs and performance metrics from Brocade switches, were collected periodically and analyzed to identify significant patterns and correlations. This was followed by preprocessing steps such as cleaning, normalization, and feature engineering. Visualization techniques were also used to gain insights into data patterns and behaviours. Each model was used to make port-wise predictions, with Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) as evaluation metrics. The results of this study will contribute to a deeper understanding of SAN data and its effective use, leading to more resilient and reliable network performance.