Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 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
Abstract (in Japanese) (See Japanese page) 
(in English) 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.
Keyword (in Japanese) (See Japanese page) 
(in English) traffic prediction / recurrent neural network / long short-term memory / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 140, CQ2018-40, pp. 51-56, July 2018.
Paper # CQ2018-40 
Date of Issue 2018-07-12 (CQ) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF CQ2018-40

Conference Information
Committee CQ  
Conference Date 2018-07-19 - 2018-07-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Tohoku Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Assessment, Measurement and Control of QoE and QoS, etc. 
Paper Information
Registration To CQ 
Conference Code 2018-07-CQ 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Traffic Matrix Prediction based on Bidirectional Recurrent Neural Network and Long Short-Term Memory 
Sub Title (in English)  
Keyword(1) traffic prediction  
Keyword(2) recurrent neural network  
Keyword(3) long short-term memory  
Keyword(4)  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Van An Le  
1st Author's Affiliation The Graduate University for Advanced Studies (Sokendai(The Graduate University for Advanced Studies))
2nd Author's Name Phi Le Nguyen  
2nd Author's Affiliation The Graduate University for Advanced Studies (Sokendai(The Graduate University for Advanced Studies))
3rd Author's Name Yusheng Ji  
3rd Author's Affiliation National Institute of Informatics (NII)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2018-07-20 13:50:00 
Presentation Time 25 minutes 
Registration for CQ 
Paper # CQ2018-40 
Volume (vol) vol.118 
Number (no) no.140 
Page pp.51-56 
#Pages 6 
Date of Issue 2018-07-12 (CQ) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan