Network Bandwidth Utilization Forecast Model on High Bandwidth Networks

With the increasing number of geographically dis-tributed 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 ap-plications. 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. Keywords—Forecasting, Network, Time series analysis I. INTRODUCTION With advances in large scale experiments and simulations, the data volume of scientific applications has rapidly grown. Even with advances in network technology, it has become more challenging to efficiently coordinate network resources and to achieve best possible network performance on a shared network. It is also challenging to build a forecast model for network bandwidth utilization with accurate and fine-grained prediction due to the computational complexities. To support efficient resource management and scheduling data movement for ever increasing data volume in extreme-scale scientific applications, we have developed an analytical model in order to characterize and forecast