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Paper Abstract and Keywords
Presentation 2022-03-10 11:40
VNF/CNF migration scheduling based on Encoder-Decoder RNN for cloud native platform
Takahiro Hirayama, Masahiro Jibiki, Takaya Miyazawa, Ved P. Kafle (NICT) IN2021-35
Abstract (in Japanese) (See Japanese page) 
(in English) The 5th generation (5G) or beyond 5G (B5G) mobile networks are required to offer various kinds of services, such as enhanced mobile broadband, ultra-reliable, ultra-low latency, and massive machine type communications, over the same infrastructure. Network function virtualization (NFV) technology enables operators to dynamically allocate computing resources to virtual or container network functions (VNFs and/or CNFs) in response to changes of users’ requests. To meet the quality of service (QoS) requirements even when the demands for resources such as CPU and bandwidth dynamically vary, the NFV system is required to find an optimal solution of migrating NFs from saturated servers to others. To quickly adapt to changes of network conditions, NF migration scheduling should be solved in a short time. However, if the system tries to find the optimal solution of the scheduling problem using the existing integer linear programming (ILP), it hard to obtain the solution in a short time because of the computation complexity. Application of machine learning (ML) technologies is an effective approach to deal with this issue. In this paper, we firstly formulate the NF migration scheduling problem as the ILP. And then, our approach is to make an artificial intelligence (AI) consisting of encoder-decoder recurrent neural network models train the solutions obtained by the above ILP. As a result of simulation, we prove that our approach can agiely provide solutions of NF migration scheduling problems with achieving both mitigation of redundant migration of NFs and reduction of computation time for training.
Keyword (in Japanese) (See Japanese page) 
(in English) Service Function Chaining (SFC) / Integer Linear Programming (ILP) / Machine Learning (ML) / Recurrent Neural Network (RNN) / Cloud Native Platform / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 434, IN2021-35, pp. 25-30, March 2022.
Paper # IN2021-35 
Date of Issue 2022-03-03 (IN) 
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 IN2021-35

Conference Information
Committee NS IN  
Conference Date 2022-03-10 - 2022-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To IN 
Conference Code 2022-03-NS-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) VNF/CNF migration scheduling based on Encoder-Decoder RNN for cloud native platform 
Sub Title (in English)  
Keyword(1) Service Function Chaining (SFC)  
Keyword(2) Integer Linear Programming (ILP)  
Keyword(3) Machine Learning (ML)  
Keyword(4) Recurrent Neural Network (RNN)  
Keyword(5) Cloud Native Platform  
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Keyword(7)  
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1st Author's Name Takahiro Hirayama  
1st Author's Affiliation National Institute of Information and Communications Technology (NICT)
2nd Author's Name Masahiro Jibiki  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
3rd Author's Name Takaya Miyazawa  
3rd Author's Affiliation National Institute of Information and Communications Technology (NICT)
4th Author's Name Ved P. Kafle  
4th Author's Affiliation National Institute of Information and Communications Technology (NICT)
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Speaker Author-1 
Date Time 2022-03-10 11:40:00 
Presentation Time 20 minutes 
Registration for IN 
Paper # IN2021-35 
Volume (vol) vol.121 
Number (no) no.434 
Page pp.25-30 
#Pages
Date of Issue 2022-03-03 (IN) 


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