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多步互联网流量预测模型与可变的预测地平线为主动的网络管理预测模型
Sajal Saha1, Anwar Haque2, Greg Sidebottom3
1Department of Computer Science, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
本研究介绍了互联网服务提供商 (ISP) 的互联网流量预测 (ITF) 模型,该模型使用异常检测和先进的算法来改善网络管理并防止拥堵.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 数据科学数据科学数据科学
背景情况:
- 互联网服务提供商 (ISP) 网络需要准确的互联网流量预测 (ITF) 来进行战略规划和网络管理.
- 现有的ITF方法与现实世界的异常数据作斗争,导致网络拥堵和过度供应.
- 积极的网络管理需要强大的预测模型,能够处理数据异常.
研究的目的:
- 开发和评估ITF创新的ITF模型,用于ISP环境中的主动网络管理.
- 通过结合异常结果的检测和缓解来解决传统ITF模型的局限性.
- 通过使用真实ISP数据,提高互联网流量预测的准确性和稳定性.
主要方法:
- 拟议的模型将异常值检测和缓解与梯度下降和提升算法 (GBR,XGB,LGB,CBR,SGD) 整合在一起.
- 该模型使用来自高速ISP网络的真实互联网流量数据进行了评估.
- 在多个预测时间段 (6,9和12个步骤) 中评估了业绩.
主要成果:
- 与传统预测模型相比,ITF开发的模型显示出更高的预测准确性.
- 集成异常值检测和缓解显著提高了模型性能和稳定性.
- 该模型证明在不同的预测时间范围内具有适应性和有效性.
结论:
- 新的ITF模型为ISP行业的积极网络管理提供了重大进步.
- 准确的预测,特别是异常值处理,对于防止网络拥堵和优化资源配置至关重要.
- 该模型在真实世界的ISP数据上的有效性验证了其实际适用性.
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