Making the AI Journey from Public Cloud to On-prem

Machine learning on array telemetry

One of our machine learning teams at Pure Storage works on a range of forecasting, regression, and classification problems. A core piece of technology we build is a predictive performance planner for our customers. It models a storage array and predicts its performance based on signals from the workload running on it. These signals include things like the read and write bandwidth, IOSize, dedupability, pattern etc.

At a high level, our system takes a collection of time series data from the past 1 to 12 months for N features and predicts a system’s performance over the next 1 to 12 months. Performance is then computed analytically in terms of a derivative of multiple system bottlenecks like CPU, SSD, IOPorts, etc. (together called “load”).

Our current model splits the problem into two halves: the first forecasts the time series of the features, and the second then uses a regression model to predict the associated load.

The time series projections are based on ARIMA and a few other detrending statistical techniques — i.e. not deep learning. We found that it was becoming hard to get this model to perform well in a large number of cases without significant tuning. As a development team, our aim is to develop a highly accurate model that we can then deploy to production.

We decided to experiment with deep learning based models to see if we could improve either our time series models or the entirety of our pipeline by doing a direct prediction of load from the time series.

The dataset consisted of ~25GB of time series data pulled from our telemetry system ( Pure1 ) and stored as a csv file. Pure1 streams telemetry data every 30 seconds from the fleet of our deployed systems. Today, we capture about 60 billion events per day.

In this post, we’ll review some of the challenges we faced — from dataset scale to the software stack to infrastructure.