Proactive service requests for forecast storage system failures

Abstract

Streaming machine telemetry (SMT) event counters are placed in critical code paths of software executing on a storage system. Each monitoring interval the values of the SMT counters are reported. When a critical error occurs on the storage system, a time series set of SMT counters from a set of previous monitoring intervals is labeled with the error type and used as a training example for a learning process. The learning process is trained to learn to learn recursions between time series sets of SMT counter values and labeled error types. Once trained, a checkpoint of the learning model is deployed as an inference model and used to predict the likely occurrence of errors before the errors occur. Predicted errors are logged into a proactive service request queue, and feedback related to the predicted errors are used as feedback to continue training the learning process.