This paper proposes a novel framework to predict traffic flows' bandwidth ahead of time. Modern network management systems share a common issue: the network situation evolves between the moment the decision is made and the moment when actions (countermeasures) are applied. This framework converts packets from real-life traffic into flows containing relevant features. Machine learning models, including Decision Tree, Random Forest, XGBoost, and Deep Neural Network, are trained on these data to predict the bandwidth at the next time instance for every flow. Predictions can be fed to the management system instead of current flows bandwidth in order to take decisions on a more accurate network state. Experiments were performed on 981,774 flows and 15 different time windows (from 0.03s to 4s). They show that the Random Forest is the best performing and most reliable model, with a predictive performance consistently better than relying on the current bandwidth (+19.73% in mean absolute error and +18.00% in root mean square error). Experimental results indicate that this framework can help network management systems to take more informed decisions using a predicted network state.
翻译:本文提出了一个新的框架, 提前预测流量的带宽。 现代网络管理系统有一个共同的问题: 网络状况在作出决定时到采取行动( 反量度) 的那一刻之间演变。 这个框架将实时流量的包转换成含有相关特征的流量。 包括决定树、 随机森林、 XGBoost 和深神经网络在内的机器学习模型, 接受了关于这些数据的培训, 以便在下次情况下预测每次流量的带宽。 预测可以提供给管理系统, 而不是提供给当前的流动带宽, 以便做出更准确的网络状态的决定。 实验是在981, 774 流动和15个不同的时间窗口( 从 0.03 至 4 ) 进行 。 它们显示随机森林是最佳和最可靠的模型, 其预测性表现始终好于当前带宽( +19.73% 的绝对误差和+18. 00% 的根正方错误) 。 实验结果表明, 这个框架可以帮助网络管理系统使用预测的网络状态做出更知情的决定 。