Dynamic Router Performance Control Utilizing Support Vector Machines for Energy Consumption Reduction

Abstract

In this paper, we propose a machine-learning-based novel dynamic performance control method for routers that supports several performance levels. The method utilizes support vector machine (SVM) to determine performance level change points. We utilize a traffic normalization technique with a corresponding performance threshold that allows us to apply the same SVM to different traffic volumes. This technique shortens the learning process for control. Several experiments on real Internet traffic data sequences prove that the method yields higher energy efficiency than conventional methods, that is, our previously proposed frequency decomposition-based method and a conventional traffic prediction method based on auto-regressive moving average model with optimal parameter values given by Akaike’s information criterion. We also evaluate the impact of several key parameters such as traffic measurement interval and the number of packet processing engines. The results clarify the proper ranges of parameter values that attain significant power reductions while keeping packet loss to acceptable levels.