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
Kejuruteraan trafik merujuk kepada teknik untuk menampung trafik dengan cekap dengan mengkonfigurasi laluan trafik secara dinamik supaya dapat menyesuaikan diri dengan perubahan trafik. Jika trafik berubah dengan kerap dan drastik, selang konfigurasi semula laluan hendaklah pendek. Walau bagaimanapun, dengan selang masa yang lebih pendek, mendapatkan maklumat trafik adalah bermasalah. Untuk mengira laluan yang sesuai, maklumat trafik yang tepat bagi keseluruhan rangkaian mesti dikumpulkan. Ini sukar dalam selang masa yang singkat, disebabkan oleh overhed yang ditanggung untuk memantau dan mengumpul maklumat trafik. Dalam makalah ini, kami mencadangkan rangka kerja untuk kejuruteraan trafik dalam kes di mana hanya sebahagian maklumat trafik boleh diperolehi dalam setiap slot masa. Rangka kerja yang dicadangkan diilhamkan oleh otak manusia, dan menggunakan kebarangkalian bersyarat untuk membuat keputusan. Dalam rangka kerja ini, pengawal digunakan untuk (1) mendapatkan jumlah maklumat trafik yang terhad, (2) menganggar dan meramalkan taburan kebarangkalian trafik, (3) mengkonfigurasi laluan dengan mempertimbangkan taburan kebarangkalian trafik yang diramalkan pada masa hadapan, dan (4) ) pilih trafik yang perlu dipantau dalam tempoh seterusnya dengan mengambil kira prestasi sistem yang dihasilkan oleh konfigurasi semula laluan. Kami menilai rangka kerja kami dengan simulasi. Keputusan menunjukkan bahawa rangka kerja kami meningkatkan kecekapan penginapan trafik walaupun hanya sebahagian maklumat trafik dipantau pada setiap slot masa.
Kodai SATAKE
Osaka University
Tatsuya OTOSHI
Osaka University
Yuichi OHSITA
Osaka University
Masayuki MURATA
Osaka University
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Salinan
Kodai SATAKE, Tatsuya OTOSHI, Yuichi OHSITA, Masayuki MURATA, "Traffic Engineering and Traffic Monitoring in the Case of Incomplete Information" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 1, pp. 111-121, January 2019, doi: 10.1587/transcom.2018EBP3049.
Abstract: Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018EBP3049/_p
Salinan
@ARTICLE{e102-b_1_111,
author={Kodai SATAKE, Tatsuya OTOSHI, Yuichi OHSITA, Masayuki MURATA, },
journal={IEICE TRANSACTIONS on Communications},
title={Traffic Engineering and Traffic Monitoring in the Case of Incomplete Information},
year={2019},
volume={E102-B},
number={1},
pages={111-121},
abstract={Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.},
keywords={},
doi={10.1587/transcom.2018EBP3049},
ISSN={1745-1345},
month={January},}
Salinan
TY - JOUR
TI - Traffic Engineering and Traffic Monitoring in the Case of Incomplete Information
T2 - IEICE TRANSACTIONS on Communications
SP - 111
EP - 121
AU - Kodai SATAKE
AU - Tatsuya OTOSHI
AU - Yuichi OHSITA
AU - Masayuki MURATA
PY - 2019
DO - 10.1587/transcom.2018EBP3049
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2019
AB - Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.
ER -