...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Future generation computer systems >IoT type-of-traffic forecasting method based on gradient boosting neural networks
【24h】

IoT type-of-traffic forecasting method based on gradient boosting neural networks

机译：基于梯度提升神经网络的物联网交通类型预测方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Network traffic classification is an important task for any current data network. There any many possible classification targets for the traffic, but we have considered as especially important the activity state of a connection and the identification of elephant flows (few connections carrying most of the traffic). With these detection targets, this work presents a modification of the gaNet architecture for classification. gaNet is an additive network model formed by 'learning blocks' that are stacked iteratively following the principles of boosting models. The original gaNet model is intended for regression, being the purpose of this work to show that it can be extended to classification under several adaptations. The resulting architecture is a generic additive network applicable to any supervised classification problem (gaNet-C). To obtain experimental results, the model is applied to a type-of-traffic forecast problem using real IoT traffic from a mobile operator. The paper presents a comprehensive comparison of results between the proposed new model and many alternative algorithms in terms of classification and performance metrics. The proposed classifier can perform a k-step ahead detection forecast based exclusively on a limited time-series of previous values for each network connection. The results include two very different challenges: detection forecast of active connections and elephant flows; showing that, in both cases, the proposed algorithm presents state of the art results.
机译：网络流量分类是任何当前数据网络的重要任务。流量有许多可能的分类目标，但是我们已经考虑到连接的活动状态和识别大象流（很少流量承载大部分流量）是特别重要的。有了这些检测目标，这项工作提出了gaNet架构的分类改进。 gaNet是由“学习模块”形成的加性网络模型，这些学习模块按照增强模型的原理进行迭代堆叠。原始的gaNet模型旨在进行回归，目的是为了表明该模型可以在多种适应下扩展到分类。最终的架构是适用于任何监督分类问题（gaNet-C）的通用加性网络。为了获得实验结果，该模型使用来自移动运营商的实际物联网流量应用于交通类型的预测问题。本文从分类和性能指标的角度对提出的新模型与许多替代算法的结果进行了全面比较。提出的分类器可以完全基于每个网络连接的先前值的有限时间序列来执行k步提前检测预测。结果包括两个截然不同的挑战：活动连接和大象流量的检测预测;表明在两种情况下，所提出的算法均能提供最新的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号