In-situ resource provisioning with adaptive scale-out for regional IoT services

Yugo Nakamura, Teruhiro Mizumoto, Hirohiko Suwa, Yutaka Arakawa, Hirozumi Yamaguchi, Keiichi Yasumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In an era where billions of IoT devices have been deployed, edge/fog computing paradigms are attracting attention for their ability to reduce processing delays and communication overhead. In order to improve Quality of Experience (QoE) of regional IoT services that create timely geo-spatial information in response to users’ queries, it is important to efficiently allocate sufficient resources based on the computational demand of each service. However since edge/fog devices are assumed to be heterogeneous (in terms of their computational power, network performance to other devices, deployment density, etc.), provisioning computational resources according to computational demand becomes a challenging constrained optimization problem. In this paper, we formulate a delay constrained regional IoT service provisioning (dcRISP) problem. dcRISP assigns computational resources of devices based on the demand of the regional IoT services in order to maximize users’ QoE. We also present dcRISP+, an extension of dcRISP, that enables resource selection to extend beyond the initial area in order to satisfy increasing computational demands. We propose a provisioning algorithm, in-situ resource area selection with adaptive scale out and in-situ task scheduling based on a tabu search, to solve the dcRISP+ problem. We conducted a simulation study of a tourist area in Kyoto where 4,000 IoT devices and 3 types of IoT services were deployed. Results show that our proposed algorithms can obtain higher user QoE compared to conventional resource provisioning algorithms.

Original languageEnglish
Title of host publicationProceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages203-213
Number of pages11
ISBN (Electronic)9781538694459
DOIs
Publication statusPublished - Dec 6 2018
Externally publishedYes
Event3rd ACM/IEEE Symposium on Edge Computing, SEC 2018 - Bellevue, United States
Duration: Oct 25 2018Oct 27 2018

Publication series

NameProceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018

Conference

Conference3rd ACM/IEEE Symposium on Edge Computing, SEC 2018
CountryUnited States
CityBellevue
Period10/25/1810/27/18

Fingerprint

Fog
Internet of things
Tabu search
Constrained optimization
Network performance
Scheduling
Communication
Processing

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

Cite this

Nakamura, Y., Mizumoto, T., Suwa, H., Arakawa, Y., Yamaguchi, H., & Yasumoto, K. (2018). In-situ resource provisioning with adaptive scale-out for regional IoT services. In Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018 (pp. 203-213). [8567667] (Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SEC.2018.00022

In-situ resource provisioning with adaptive scale-out for regional IoT services. / Nakamura, Yugo; Mizumoto, Teruhiro; Suwa, Hirohiko; Arakawa, Yutaka; Yamaguchi, Hirozumi; Yasumoto, Keiichi.

Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 203-213 8567667 (Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nakamura, Y, Mizumoto, T, Suwa, H, Arakawa, Y, Yamaguchi, H & Yasumoto, K 2018, In-situ resource provisioning with adaptive scale-out for regional IoT services. in Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018., 8567667, Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 203-213, 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018, Bellevue, United States, 10/25/18. https://doi.org/10.1109/SEC.2018.00022
Nakamura Y, Mizumoto T, Suwa H, Arakawa Y, Yamaguchi H, Yasumoto K. In-situ resource provisioning with adaptive scale-out for regional IoT services. In Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 203-213. 8567667. (Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018). https://doi.org/10.1109/SEC.2018.00022
Nakamura, Yugo ; Mizumoto, Teruhiro ; Suwa, Hirohiko ; Arakawa, Yutaka ; Yamaguchi, Hirozumi ; Yasumoto, Keiichi. / In-situ resource provisioning with adaptive scale-out for regional IoT services. Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 203-213 (Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018).
@inproceedings{1fda621299e244bc98f6431d0b5aefd6,
title = "In-situ resource provisioning with adaptive scale-out for regional IoT services",
abstract = "In an era where billions of IoT devices have been deployed, edge/fog computing paradigms are attracting attention for their ability to reduce processing delays and communication overhead. In order to improve Quality of Experience (QoE) of regional IoT services that create timely geo-spatial information in response to users’ queries, it is important to efficiently allocate sufficient resources based on the computational demand of each service. However since edge/fog devices are assumed to be heterogeneous (in terms of their computational power, network performance to other devices, deployment density, etc.), provisioning computational resources according to computational demand becomes a challenging constrained optimization problem. In this paper, we formulate a delay constrained regional IoT service provisioning (dcRISP) problem. dcRISP assigns computational resources of devices based on the demand of the regional IoT services in order to maximize users’ QoE. We also present dcRISP+, an extension of dcRISP, that enables resource selection to extend beyond the initial area in order to satisfy increasing computational demands. We propose a provisioning algorithm, in-situ resource area selection with adaptive scale out and in-situ task scheduling based on a tabu search, to solve the dcRISP+ problem. We conducted a simulation study of a tourist area in Kyoto where 4,000 IoT devices and 3 types of IoT services were deployed. Results show that our proposed algorithms can obtain higher user QoE compared to conventional resource provisioning algorithms.",
author = "Yugo Nakamura and Teruhiro Mizumoto and Hirohiko Suwa and Yutaka Arakawa and Hirozumi Yamaguchi and Keiichi Yasumoto",
year = "2018",
month = "12",
day = "6",
doi = "10.1109/SEC.2018.00022",
language = "English",
series = "Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "203--213",
booktitle = "Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018",
address = "United States",

}

TY - GEN

T1 - In-situ resource provisioning with adaptive scale-out for regional IoT services

AU - Nakamura, Yugo

AU - Mizumoto, Teruhiro

AU - Suwa, Hirohiko

AU - Arakawa, Yutaka

AU - Yamaguchi, Hirozumi

AU - Yasumoto, Keiichi

PY - 2018/12/6

Y1 - 2018/12/6

N2 - In an era where billions of IoT devices have been deployed, edge/fog computing paradigms are attracting attention for their ability to reduce processing delays and communication overhead. In order to improve Quality of Experience (QoE) of regional IoT services that create timely geo-spatial information in response to users’ queries, it is important to efficiently allocate sufficient resources based on the computational demand of each service. However since edge/fog devices are assumed to be heterogeneous (in terms of their computational power, network performance to other devices, deployment density, etc.), provisioning computational resources according to computational demand becomes a challenging constrained optimization problem. In this paper, we formulate a delay constrained regional IoT service provisioning (dcRISP) problem. dcRISP assigns computational resources of devices based on the demand of the regional IoT services in order to maximize users’ QoE. We also present dcRISP+, an extension of dcRISP, that enables resource selection to extend beyond the initial area in order to satisfy increasing computational demands. We propose a provisioning algorithm, in-situ resource area selection with adaptive scale out and in-situ task scheduling based on a tabu search, to solve the dcRISP+ problem. We conducted a simulation study of a tourist area in Kyoto where 4,000 IoT devices and 3 types of IoT services were deployed. Results show that our proposed algorithms can obtain higher user QoE compared to conventional resource provisioning algorithms.

AB - In an era where billions of IoT devices have been deployed, edge/fog computing paradigms are attracting attention for their ability to reduce processing delays and communication overhead. In order to improve Quality of Experience (QoE) of regional IoT services that create timely geo-spatial information in response to users’ queries, it is important to efficiently allocate sufficient resources based on the computational demand of each service. However since edge/fog devices are assumed to be heterogeneous (in terms of their computational power, network performance to other devices, deployment density, etc.), provisioning computational resources according to computational demand becomes a challenging constrained optimization problem. In this paper, we formulate a delay constrained regional IoT service provisioning (dcRISP) problem. dcRISP assigns computational resources of devices based on the demand of the regional IoT services in order to maximize users’ QoE. We also present dcRISP+, an extension of dcRISP, that enables resource selection to extend beyond the initial area in order to satisfy increasing computational demands. We propose a provisioning algorithm, in-situ resource area selection with adaptive scale out and in-situ task scheduling based on a tabu search, to solve the dcRISP+ problem. We conducted a simulation study of a tourist area in Kyoto where 4,000 IoT devices and 3 types of IoT services were deployed. Results show that our proposed algorithms can obtain higher user QoE compared to conventional resource provisioning algorithms.

UR - http://www.scopus.com/inward/record.url?scp=85060200543&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060200543&partnerID=8YFLogxK

U2 - 10.1109/SEC.2018.00022

DO - 10.1109/SEC.2018.00022

M3 - Conference contribution

AN - SCOPUS:85060200543

T3 - Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018

SP - 203

EP - 213

BT - Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -