Modularized and Contract-Based Prediction Models in Programmable Networks

Abstract

Network traffic engineering aims at the network quality, optimizing routes and detecting network attacks. In this context, traffic prediction is an essential tool to capture the underlying behavior of a network. Therefore, this work proposes a modularization architecture for volumetric prediction models, allowing switching between models and setups at runtime in controllers of Software Defined Networks (SDN), dealing with short time series and delivering the data already processed for the prediction. The proposed architecture compares the results from four traditional predictors based on short-range time dependency.