An Analysis of Traffic Load Prediction Base on Auto Regressive Model in Small Time Granularity

Abstract

Traffic load measurement and prediction is an important component of Quality of Service (QoS) in network management and traffic engineering. Especially to some real time methods in order to ensure QoS, such as Admission Control and Resource Reservation and so on, better traffic load prediction results can improve their work efficiency greatly and deeply improve network bandwidth utilization and ensure better QoS. So we regard that efficient and effective traffic load prediction techniques are desirable necessary. Much former research work is analyzing traffic load auto regressive characteristic in large time granularity, such as day, week or month and so on, but they couldn't be used in these real time methods including admission control and resource reservation. So we analyze the self-similarity of traffic load in small time granularity and propose a prediction method based on Auto Regressive Model. In the simulation, we adopt the real traffic load of NLANR and the simulation results have proved that the probability of prediction error less than 15% is about 90%.