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As large scale cloud computing centers become more popular than individualservers, predicting future resource demand need has become an importantproblem. Forecasting resource need allows public cloud providers to proactivelyallocate or deallocate resources for cloud services. This work seeks to predictone resource, CPU usage, over both a short term and long term time scale.  To gain insight into the model characteristics that best support specifictasks, we consider two vastly different architectures: the historicallyrelevant SARIMA model and the more modern neural network, LSTM model. We applythese models to Azure data resampled to 20 minutes per data point with the goalof predicting usage over the next hour for the short-term task and for the nextthree days for the long-term task. The SARIMA model outperformed the LSTM forthe long term prediction task, but performed poorer on the short term task.Furthermore, the LSTM model was more robust, whereas the SARIMA model relied onthe data meeting certain assumptions about seasonality.

Using LSTM and SARIMA Models to Forecast Cluster CPU Usage