论文标题
使用Kalman过滤器改善了Bittorrent交通预测的性能
Improved Performance of BitTorrent Traffic Prediction Using Kalman Filter
论文作者
论文摘要
监督Internet流量对于任何Internet服务提供商（ISP）至关重要，以优化的方式动态分配带宽。 Bittorrent是庞大文件传输的众所周知的点对点文件共享协议。其广泛的带宽消耗会影响服务质量（QoS），并导致其他应用程序的延迟。有很大的要求需要预测Bittorrent流量以改善QoS。在本文中，我们提出了一种基于卡尔曼过滤器（KF）的方法，以预测各种流量数据集的网络流量。与自动回归移动平均值（ARMA）模型相比，在平方误差（MSE）和总计算时间方面，KF的性能都优异。
Supervising internet traffic is essential for any Internet Service Provider (ISP) to dynamically allocate bandwidth in an optimized manner. BitTorrent is a well-known peer-to-peer file-sharing protocol for bulky file transfer. Its extensive bandwidth consumption affects the Quality of Service (QoS) and causes latency to other applications. There is a strong requirement to predict the BitTorrent traffic to improve the QoS. In this paper, we propose a Kalman filter (KF) based method to predict the network traffic for various traffic data sets. The observed performance of KF is superior in terms of both Mean Squared Error (MSE) and total computation time, when compared to Auto Regressive Moving Average (ARMA) model.
