基于格蘭杰因果關系貝葉斯網絡的大規(guī)模無線局域網流量預測方法
基于格蘭杰因果關系貝葉斯網絡的大規(guī)模無線局域網流量預測方法
(武漢第二船舶設計研究所武漢430064)
摘要：研究了大規(guī)模無線局域網內的流量特性，發(fā)現(xiàn)不同接入點間的流量存在格蘭杰因果關系。流量的格蘭杰岡果關系說明，可以通過多個存在因果關系的接入點的歷史流量，提高對目標接人點的當前流量預測的準確性。通過貝葉斯網絡對存在因果關系的接人點流量進行建模，并利用多個接入點的歷史流量對目標接人點的流量進行預測，提高了預測的準確性。最后，通過接入點數量大于100個的無線局域網的實際流量數據，驗證了該方法的有效性及準確性，建立了一套完整的數據特征分析、建模及預測的流量數據處理流程。
關鍵詞：無線局域網；流量預測；流量特性；格蘭杰因果關系；貝葉斯網絡
doi: 10.1195 9/j.issn.1000-0801.2015 201
Predicting Large-Scale WLAN Traffic via Granger Causality Based Bayesian Network
Wang Hao, Lv Yunfei, Chen Yuanbao, Peng Yunfei
(
Abstract: Granger causality existed between traffic at different access points of large-scale wireless LANs wasdiscovered. The Granger causality illustrates that the historical traffic of access points that exist causality withintarget access points help predict the future of target access points with better accuracy than when considering information from the past of target access point alone. Bayesian network to model the causal relationship between access points and adopted a Gaussian mixture model (GMM) was used, as well as a weighted combination of several normal distribution functions in order to approximate the joint probability distribution in Bayesian networks. Finally, the traffic data in large-scale wireless LANs was imported, having hundreds of access points, to verify the accuracy of the proposed method, and a processing flow of analysis, modeling and prediction of traffic flow data was established.
Key words: WLAN, traffic prediction, traffic characteristics, Granger causality, Bayesian network
結論
本文研究了兩個大規(guī)模無線局域網內的流量特性，發(fā)現(xiàn)AP間廣泛存在格蘭杰因果關系，并且存在格蘭杰因果關系的接人點位置集中在一階與二階鄰居AP中。然后，本文通過貝葉斯網絡利用格蘭杰因果關系特性，對存在因果關系的接人點流量進行建模，并通過貝葉斯網絡模型利用多個接入點的歷史流量對目標接入點的流量進行預測，提高了預測的準確性。最后，通過接入點數量大于100個的無線局域網的實際流量數據，驗證了該方法相較于WARIMA模型的準確性，并驗證了該方法在不同場景下的有效性，建立了一套完整的數據特征分析、建模及預測的流量數據處理流程。
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