The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ramalan throughput adalah salah satu teknik yang menjanjikan untuk meningkatkan kualiti perkhidmatan (QoS) dan kualiti pengalaman (QoE) aplikasi mudah alih. Untuk menangani masalah meramalkan taburan daya tampung masa hadapan dengan tepat semasa keseluruhan sesi, yang boleh mempamerkan turun naik daya tampung yang besar dalam senario yang berbeza (terutamanya senario pengguna bergerak), kami mencadangkan kaedah ramalan daya tampung berasaskan sejarah yang menggunakan analisis siri masa dan teknik pembelajaran mesin. untuk komunikasi rangkaian mudah alih. Kaedah ini dipanggil Ramalan Hibrid dengan Model Autoregresif dan Model Markov Tersembunyi (HOAH). Berbeza daripada kaedah sedia ada, HOAH menggunakan Mesin Vektor Sokongan (SVM) untuk mengklasifikasikan peralihan pemprosesan kepada dua kelas, dan meramalkan pemprosesan protokol kawalan penghantaran (TCP) dengan menukar antara Model Autoregresif (Model AR) dan Gaussian Mixture Model-Hidden Markov. Model (GMM-HMM). Kami menjalankan eksperimen lapangan untuk menilai kaedah yang dicadangkan dalam tujuh senario berbeza. Keputusan menunjukkan HOAH boleh meramalkan daya pengeluaran masa hadapan dengan berkesan dan mengurangkan ralat ramalan sebanyak maksimum 55.95% berbanding kaedah lain.
Bo WEI
Waseda University
Kenji KANAI
Waseda University
Wataru KAWAKAMI
Waseda University
Jiro KATTO
Waseda University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Bo WEI, Kenji KANAI, Wataru KAWAKAMI, Jiro KATTO, "HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 7, pp. 1612-1624, July 2018, doi: 10.1587/transcom.2017CQP0007.
Abstract: Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017CQP0007/_p
Salinan
@ARTICLE{e101-b_7_1612,
author={Bo WEI, Kenji KANAI, Wataru KAWAKAMI, Jiro KATTO, },
journal={IEICE TRANSACTIONS on Communications},
title={HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks},
year={2018},
volume={E101-B},
number={7},
pages={1612-1624},
abstract={Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.},
keywords={},
doi={10.1587/transcom.2017CQP0007},
ISSN={1745-1345},
month={July},}
Salinan
TY - JOUR
TI - HOAH: A Hybrid TCP Throughput Prediction with Autoregressive Model and Hidden Markov Model for Mobile Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1612
EP - 1624
AU - Bo WEI
AU - Kenji KANAI
AU - Wataru KAWAKAMI
AU - Jiro KATTO
PY - 2018
DO - 10.1587/transcom.2017CQP0007
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2018
AB - Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.
ER -