Using Network Application Behavior to Predict Performance

Today`s continuously growing Internet requires users and network applications to have knowledge of network metrics. This knowledge is critical for decision making during the usage of network applications. This thesis studies application related network metrics. The major approach in this work is to examine the traffic between a simulated user and network applications. We use the historical data collected from previous usage of network applications to make predictions for future usage of those applications. We also use the historical data obtained from a given application to make predictions about another application. Prediction mechanisms require us to make parameter choices so that certain weights can be placed on historical data versus current data. We study these different choices and use the values from our best experimental results. From these studies we conclude that our data prediction is quite accurate and remains stable over a range of parameter choices. The use of shared routing paths between users and network applications are explored in the performance prediction of applications. Only some servers at the same locations show similar prediction results. The network applications studied are also varied, including web, streaming, DNS, etc. We see whether sharing information obtained from different applications can be used to make predictions of application performance. However, we observe limited success in predictions across applications