| IP骨干网流量的自动化预测 |
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| 引用本文： | 韦烜,阮科,黄晓莹,陈迅,黄灿灿. IP骨干网流量的自动化预测[J]. 电信科学, 2020, 36(8): 175-183. DOI: 10.11959/j.issn.1000-0801.2020153 |
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| 作者姓名： | 韦烜 阮科 黄晓莹 陈迅 黄灿灿 |
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| 作者单位： | 中国电信股份有限公司研究院，广东 广州 510630 |
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| 摘 要： | 高效、可靠的网络流量预测是网络规划、扩容建设的基础。互联网流量目前缺乏完备的理论模型，行业内大多根据工程实践特点，设计简化可操作的预测模型以满足IP网络规划需求。首先根据中国电信自身IP骨干网流量预测工作的需求及特点，使用时间序列分析的多因子回归模型和函数自适应模型对IP骨干网流量进行分析和预测，基于大量现网实际数据的仿真运算，对比两种模型的特点、优劣和适用场景，提出了一种预测模型选择和参数优化的原则和方法。在此基础上，构建了可以满足百千量级时间序列要求的自动化流量预测系统，极大简化并提升了流量预测工作的效率。最后，展望了未来IP流量预测工作的延展方向和关注重点。
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| 关 键 词： | 时间序列 流量预测 预测模型 |
Automatic prediction for IP backbone network traffic |
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| Xuan WEI,Ke RUAN,Xiaoying HUANG,Xun CHEN,Cancan HUANG. Automatic prediction for IP backbone network traffic[J]. Telecommunications Science, 2020, 36(8): 175-183. DOI: 10.11959/j.issn.1000-0801.2020153 |
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| Authors: | Xuan WEI Ke RUAN Xiaoying HUANG Xun CHEN Cancan HUANG |
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| Affiliation: | Research Institute of China Telecom Co.,Ltd.,Guangzhou 510630,China |
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| Abstract: | Efficient and reliable network traffic prediction is the basis of network planning and capacity expansion construction.Currently,there is no integral theoretical model to describe internet traffic.Most of the industry designs simplified and operable prediction models.Firstly,according to the characteristics of China Telecom’s IP backbone network traffic and its planning requirements,the IP backbone network traffic was analyzed and forecasted by using the multi-factor regression model and the function adaptive mode of time series.The characteristics,advantages,disadvantages and applicable scenarios of these two models were compared based on simulation of a large number of actual network data.A set of principles and methods for selecting prediction model and optimizing parameters were proposed.Then,an automatic forecasting system with the high performance of dealing with hundreds of time series was built to greatly simplify and improve the traffic prediction efficiency.Finally,the development orientation of network capacity extension and key points of future IP traffic prediction were prospected. |
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| Keywords: | time series traffic forecast prediction model |
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