Analysis the self-similarity of network traffic in fractional Fourier transform domain
Statistical characteristics of network traffic data in FrFT domain were analyzed,which indicates the self-simi-larity feature.Further,Hurst parameter estimation methods based on modified ensemble empirical mode decomposi-tion-detrended fluctuation analysis (MEEMD-DFA) and adaptive estimator with wei...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2013-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436X.2013.06.005/ |
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| _version_ | 1841539837833773056 |
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| author | Tong GUO Ju-long LAN Wan-wei HUANG Zhen ZHANG |
| author_facet | Tong GUO Ju-long LAN Wan-wei HUANG Zhen ZHANG |
| author_sort | Tong GUO |
| collection | DOAJ |
| description | Statistical characteristics of network traffic data in FrFT domain were analyzed,which indicates the self-simi-larity feature.Further,Hurst parameter estimation methods based on modified ensemble empirical mode decomposi-tion-detrended fluctuation analysis (MEEMD-DFA) and adaptive estimator with weighted least square regression (WLSR) were presented,which aimed at displaying network traffic in “time” or “frequency” domain of FrFT domain separately.Experimental results demonstrate that the MEEMD-DFA method has more accurate estimate precision but higher com-putational complexity than existing common methods.The overall robustness of adaptive estimator is more satisfactory than that of the other methods in simulation,while it has lower computational complexity.Thus,it can be used as a real-time online Hurst parameter estimator for traffic data. |
| format | Article |
| id | doaj-art-c82aa6513ae047ac9d56a15be2c0f59e |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2013-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-c82aa6513ae047ac9d56a15be2c0f59e2025-01-14T06:35:27ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2013-06-0134384859672600Analysis the self-similarity of network traffic in fractional Fourier transform domainTong GUOJu-long LANWan-wei HUANGZhen ZHANGStatistical characteristics of network traffic data in FrFT domain were analyzed,which indicates the self-simi-larity feature.Further,Hurst parameter estimation methods based on modified ensemble empirical mode decomposi-tion-detrended fluctuation analysis (MEEMD-DFA) and adaptive estimator with weighted least square regression (WLSR) were presented,which aimed at displaying network traffic in “time” or “frequency” domain of FrFT domain separately.Experimental results demonstrate that the MEEMD-DFA method has more accurate estimate precision but higher com-putational complexity than existing common methods.The overall robustness of adaptive estimator is more satisfactory than that of the other methods in simulation,while it has lower computational complexity.Thus,it can be used as a real-time online Hurst parameter estimator for traffic data.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436X.2013.06.005/self-similarityfractional Fourier transformHurst parameterensemble empirical mode decompositionweighted least square regressionadaptive |
| spellingShingle | Tong GUO Ju-long LAN Wan-wei HUANG Zhen ZHANG Analysis the self-similarity of network traffic in fractional Fourier transform domain Tongxin xuebao self-similarity fractional Fourier transform Hurst parameter ensemble empirical mode decomposition weighted least square regression adaptive |
| title | Analysis the self-similarity of network traffic in fractional Fourier transform domain |
| title_full | Analysis the self-similarity of network traffic in fractional Fourier transform domain |
| title_fullStr | Analysis the self-similarity of network traffic in fractional Fourier transform domain |
| title_full_unstemmed | Analysis the self-similarity of network traffic in fractional Fourier transform domain |
| title_short | Analysis the self-similarity of network traffic in fractional Fourier transform domain |
| title_sort | analysis the self similarity of network traffic in fractional fourier transform domain |
| topic | self-similarity fractional Fourier transform Hurst parameter ensemble empirical mode decomposition weighted least square regression adaptive |
| url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436X.2013.06.005/ |
| work_keys_str_mv | AT tongguo analysistheselfsimilarityofnetworktrafficinfractionalfouriertransformdomain AT julonglan analysistheselfsimilarityofnetworktrafficinfractionalfouriertransformdomain AT wanweihuang analysistheselfsimilarityofnetworktrafficinfractionalfouriertransformdomain AT zhenzhang analysistheselfsimilarityofnetworktrafficinfractionalfouriertransformdomain |