Prediction of Web Goodput Using Nonlinear Autoregressive Models

摘要

The performance prediction is a key part of the modern network traffic engineering. In this paper we present the application of nonlinear autoregressive modeling to the prediction of goodput level in web transactions. We propose the two-stage approach, with clustering step on historical data, prior to classification, to determine the most appropriate traffic intensity levels. Our study is based on the data collected by the MWING system, an ensemble of web performance measurement agents, and cover over a year of continuous observations of a group of HTTP servers.