Automatic prediction for IP backbone network traffic
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 charact...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2020-08-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2020153 |
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| _version_ | 1850212952414617600 |
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| author | Xuan WEI Ke RUAN Xiaoying HUANG Xun CHEN Cancan HUANG |
| author_facet | Xuan WEI Ke RUAN Xiaoying HUANG Xun CHEN Cancan HUANG |
| author_sort | Xuan WEI |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-86e947d72b9344a18c146b4fd8ea685e |
| institution | OA Journals |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2020-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-86e947d72b9344a18c146b4fd8ea685e2025-08-20T02:09:13ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012020-08-013617518359811938Automatic prediction for IP backbone network trafficXuan WEIKe RUANXiaoying HUANGXun CHENCancan HUANGEfficient 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.http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2020153time series;traffic forecast;prediction model |
| spellingShingle | Xuan WEI Ke RUAN Xiaoying HUANG Xun CHEN Cancan HUANG Automatic prediction for IP backbone network traffic Dianxin kexue time series;traffic forecast;prediction model |
| title | Automatic prediction for IP backbone network traffic |
| title_full | Automatic prediction for IP backbone network traffic |
| title_fullStr | Automatic prediction for IP backbone network traffic |
| title_full_unstemmed | Automatic prediction for IP backbone network traffic |
| title_short | Automatic prediction for IP backbone network traffic |
| title_sort | automatic prediction for ip backbone network traffic |
| topic | time series;traffic forecast;prediction model |
| url | http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2020153 |
| work_keys_str_mv | AT xuanwei automaticpredictionforipbackbonenetworktraffic AT keruan automaticpredictionforipbackbonenetworktraffic AT xiaoyinghuang automaticpredictionforipbackbonenetworktraffic AT xunchen automaticpredictionforipbackbonenetworktraffic AT cancanhuang automaticpredictionforipbackbonenetworktraffic |