Predicting End-to-end Network Load

Abstract

Due to their limited and fluctuating bandwidth, mobile ad hoc networks (MANETs) are inherently resource-constrained. As traffic load increases, we need to decide when to throttle the traffic to maximize user satisfaction while keeping the network operational. The state-of-the-art for making these decisions is based on network measurements and so employs a reactive approach to deteriorating network state by reducing the amount of traffic admitted into the network. However, a better approach is to avoid congestion before it occurs by predicting future network traffic using user and application information from the overlaying social network. We use machine learning methods to predict the source and destination of near future traffic load.