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Paper Abstract and Keywords
Presentation 2020-12-18 16:25
Performance evaluation of using image processing by Fog Computing
Shintaro Hirose (Niigata Univ.), Takafumi Hayashi (Nihon Univ.) NS2020-107
Abstract (in Japanese) (See Japanese page) 
(in English) With the spread of IoT and the advent of 5G, the amount of data handled on the Internet has become huge. Due to the increase in communication traffic and demand for low-latency messaging, the current mainstream of cloud computing will reach its limits, and it will be necessary to migrate to a new network architecture. In this situation, Fog Computing, as proposed by Cisco, is considered to be one of the solutions to these problems. This concept places a fog server between the cloud and the user to perform distributed processing, which prevents the concentration of data processing and enables us to deal with increasing data traffic. In this paper, we evaluate the inference performance of a machine learning model that interacts with fog and cloud. We experiment using Docker to prepare fog nodes and clouds with different amounts of memory, and change the communication path to simulate a server machine as a fog and cloud. We compare the processing time between fog and cloud based on the regional object detection model, and the processing time by the input format using the image classification model, and report on the usefulness of fog use and effective input formats in image processing inference.
Keyword (in Japanese) (See Japanese page) 
(in English) Fog Computing / TensorFlow Serving / Base64 / Performance evaluation / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 297, NS2020-107, pp. 108-112, Dec. 2020.
Paper # NS2020-107 
Date of Issue 2020-12-10 (NS) 
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 NS2020-107

Conference Information
Committee NS RCS  
Conference Date 2020-12-17 - 2020-12-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Multi-hop/Relay/Cooperation, Disaster-resistant wireless network, Sensor/Mesh network, Ad-hoc network, D2D/M2M, Wireless network coding, Handover/AP switching/Connected cell control/Load balancing among base stations/Mobile network dynamic reconfiguration, QoS/QoE assurance, Wireless VoIP, IoT, Edge computing, etc. 
Paper Information
Registration To NS 
Conference Code 2020-12-NS-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Performance evaluation of using image processing by Fog Computing 
Sub Title (in English)  
Keyword(1) Fog Computing  
Keyword(2) TensorFlow Serving  
Keyword(3) Base64  
Keyword(4) Performance evaluation  
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1st Author's Name Shintaro Hirose  
1st Author's Affiliation Niigata University (Niigata Univ.)
2nd Author's Name Takafumi Hayashi  
2nd Author's Affiliation Nihon University (Nihon Univ.)
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Speaker Author-1 
Date Time 2020-12-18 16:25:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2020-107 
Volume (vol) vol.120 
Number (no) no.297 
Page pp.108-112 
#Pages 5 
Date of Issue 2020-12-10 (NS) 

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