Large Data Transfer Predictability and Forecasting using Application-Aware SDN

Abstract

Network management for applications that rely on large-scale data transfers is challenging due to the volatility and the dynamic nature of the access traffic patterns. Predictive analytics and forecasting play an important role in providing effective resource allocation strategies for large data transfers. We propose a predictive analytics solution for large data transfers using an application-aware software defined networking (SDN) approach. We perform extensive exploratory data analysis to characterize the GridFTP connection transfers dataset and present various strategies for its use with statistical forecasting models. We develop a univariate autoregressive integrated moving average (ARIMA) based prediction framework for forecasting GridFTP connection transfers. Our prediction model tightly integrates with an application-aware SDN solution to preemptively drive network management decisions for GridFTP resource allocation at a U.S. CMS Tier-2 site. Further, our framework has a mean absolute percentage error (MAPE) ranging from 6% to 10% when applied to make rolling forecasts.