| 電子情報通信学会 研究会発表申込システム 講演論文 詳細 |
技報閲覧サービス [ログイン] 技報アーカイブ |
| トップに戻る | 前のページに戻る | [Japanese] / [English] |
| 講演抄録／キーワード | ||
| 講演名 | 2018-07-20 13:50 Traffic Matrix Prediction based on Bidirectional Recurrent Neural Network and Long Short-Term Memory ○Van An Le・Phi Le Nguyen（Sokendai(The Graduate University for Advanced Studies)）・Yusheng Ji（NII）  CQ2018-40 |
|
| 抄録 | （和） | Accurate prediction of the future network traffic plays an important role in various network problems (e.g. traffic engineering, capacity planning, quality of service provisioning, etc.). Measuring all the network traffic is impossible or impractical due to the monitoring resources constraints as well as the dynamic of temporal/spatial fluctuations of the traffic. To this end, a common approach is to solve the traffic matrix interpolation using compress sensing and matrix completion. Besides that, there are some studies exploiting Deep Learning techniques such as Restricted Boltzmann Machine or Recurrent Neural Network to estimate the traffic volume. However, their proposal reveals a poor performance regarding the traffic inference when the measurement data has a highly missing rate. In this paper, we propose a highly accurate traffic prediction algorithm by leveraging the advantages of Long Short-Term Memory (LSTM) in the time series estimation and modifying the Bidirectional Recurrent Neural Networks (BRNN) in correcting the feeding data. We evaluate our model based on the Abilene Dataset which contains the real traffic matrices. The experiment results show that the proposed approach can achieve significantly better prediction accuracy in term of several metrics such as error ratio, mean absolute error and root mean square error, even when only 30% of the traffic flows in the network are measured. |
| （英） | Accurate prediction of the future network traffic plays an important role in various network problems (e.g. traffic engineering, capacity planning, quality of service provisioning, etc.). Measuring all the network traffic is impossible or impractical due to the monitoring resources constraints as well as the dynamic of temporal/spatial fluctuations of the traffic. To this end, a common approach is to solve the traffic matrix interpolation using compress sensing and matrix completion. Besides that, there are some studies exploiting Deep Learning techniques such as Restricted Boltzmann Machine or Recurrent Neural Network to estimate the traffic volume. However, their proposal reveals a poor performance regarding the traffic inference when the measurement data has a highly missing rate. In this paper, we propose a highly accurate traffic prediction algorithm by leveraging the advantages of Long Short-Term Memory (LSTM) in the time series estimation and modifying the Bidirectional Recurrent Neural Networks (BRNN) in correcting the feeding data. We evaluate our model based on the Abilene Dataset which contains the real traffic matrices. The experiment results show that the proposed approach can achieve significantly better prediction accuracy in term of several metrics such as error ratio, mean absolute error and root mean square error, even when only 30% of the traffic flows in the network are measured. |
|
| キーワード | （和） | traffic prediction / recurrent neural network / long short-term memory / / / / / |
| （英） | traffic prediction / recurrent neural network / long short-term memory / / / / / | |
| 文献情報 | 信学技報, vol. 118, no. 140, CQ2018-40, pp. 51-56, 2018年7月. | |
| 資料番号 | CQ2018-40 | |
| 発行日 | 2018-07-12 (CQ) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | CQ2018-40 |
|
| 研究会情報 | |
| 研究会 | CQ |
| 開催期間 | 2018-07-19 - 2018-07-20 |
| 開催地（和） | 東北大学 |
| 開催地（英） | Tohoku Univ. |
| テーマ（和） | QoEとQoSの評価・計測・制御，一般 |
| テーマ（英） | Assessment, Measurement and Control of QoE and QoS, etc. |
| 講演論文情報の詳細 | |
| 申込み研究会 | CQ |
| 会議コード | 2018-07-CQ |
| 本文の言語 | 英語 |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Traffic Matrix Prediction based on Bidirectional Recurrent Neural Network and Long Short-Term Memory |
| サブタイトル（英） | |
| キーワード(1)（和/英） | traffic prediction / traffic prediction |
| キーワード(2)（和/英） | recurrent neural network / recurrent neural network |
| キーワード(3)（和/英） | long short-term memory / long short-term memory |
| キーワード(4)（和/英） | / |
| キーワード(5)（和/英） | / |
| キーワード(6)（和/英） | / |
| キーワード(7)（和/英） | / |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | Van An Le / Van An Le / |
| 第1著者 所属（和/英） | 総合研究大学院大学 (略称： 総研大) The Graduate University for Advanced Studies (略称： Sokendai(The Graduate University for Advanced Studies)) |
| 第2著者 氏名（和/英/ヨミ） | Phi Le Nguyen / Phi Le Nguyen / |
| 第2著者 所属（和/英） | 総合研究大学院大学 (略称： 総研大) The Graduate University for Advanced Studies (略称： Sokendai(The Graduate University for Advanced Studies)) |
| 第3著者 氏名（和/英/ヨミ） | 計 宇生 / Yusheng Ji / |
| 第3著者 所属（和/英） | 国立情報学研究所 (略称： NII) National Institute of Informatics (略称： NII) |
| 講演者 | 第1著者 |
| 発表日時 | 2018-07-20 13:50:00 |
| 発表時間 | 25分 |
| 申込先研究会 | CQ |
| 資料番号 | CQ2018-40 |
| 巻番号（vol） | vol.118 |
| 号番号（no） | no.140 |
| ページ範囲 | pp.51-56 |
| ページ数 | 6 |
| 発行日 | 2018-07-12 (CQ) |
[研究会発表申込システムのトップページに戻る]