Lawrence Berkeley National Laboratory

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. We have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate forecast model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage changes. Its forecast errors are within the standard deviation of the monitored measurements.