Forecasting the Stability of the Data Centre Based on Real-Time Data of Batch Workload Using Times Series Models

Abstract

Forecasting has diverse range of applications in many fields like weather, stock market, etc. The main highlight of this work is to forecast the values of the given metric for near future and predict the stability of the Data Centre based on the usage of that metric. Since the parameters that are being monitored in a Data Centre are large, an accurate forecasting is essential for the Data Centre architects in order to make necessary upgrades in a server system. The major criteria that result in SLA violation and loss to a particular business are peak values in performance parameters and resource utilization; hence it is very important that the peak values in performance, resource and workload be forecasted. Here, we mainly concentrate on the metric batch workload of a real-time Data Centre. In this work, we mainly focused on forecasting the batch workload using the auto regressive integrated moving average (ARIMA) model and exponential smoothing and predicted the stability of the Data Centre for the next 6 months. Further, we have performed a comparison of ARIMA model and exponential smoothing and we arrived at the conclusion that ARIMA model outperformed the other. The best model is selected based on the ACF residual correlogram, Forecast Error histogram and the error measures like root mean square error (RMSE), mean absolute error (MAE), mean absolute scale error (MASE) and p-value of Ljung-Box statistics. From the above results we conclude that ARIMA model is the best model for forecasting this time series data and hence based on the ARIMA models forecast result we predicted the stability of the Data Centre for the next 6 months.