Internet Path Behavior Prediction via Data Mining: Conceptual Framework and Case Study
Leszek Borzemski (Wroclaw University of Technology, Poland)
Abstract: In this paper we propose an application of data mining methods in the prediction of the availability and performance of Internet paths. We deploy a general decision-making method for advising the users in further usage of Internet path at particular time and date. The method is based on the clustering and tree classification data mining techniques. The usefulness of our method for prediction the Internet path behavior has been confirmed in real-life experiment. The active Internet measurements were performed to gather the end-to-end latency and packet routing information. The knowledge gathered has been analyzed using a professional data mining package via neural clustering and decision tree algorithms. The results show that the data mining can be efficiently used for the purpose of the forecasting the network behavior. We propose to build a network performance monitoring and prediction service based on proposed data mining procedure. We address our approach especially to the non-networkers of such networking frameworks as Grid and overlay networks who want to schedule their network activity but who want to be left free from networking issues to concentrate on their work.
Keywords: Internet performance, Knowledge Management, data mining, end-to-end-performance, grids, network behavior prediction
Categories: C.2.3, C.4, H.1.2, H.2.8, M.0, M.1, M.7