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
N1 - Funding Information:
In this paper, we formulated the delay constrained regional IoT service provisioning (dcRISP) problem of assigning tasks for processing queries for geo-spatial information to IoT devices in the target regional area and its extended version, dcRISP+ problem that allows the extension of the resource selection area. To solve these problems, we proposed the adaptive in-situ task scheduling algorithm composed of in-situ resource area selection with adaptive scale out and in-situ task scheduling based on tabu search technique. We evaluated the proposed methods through computer simulations supposing a regional area with 4,000 IoT devices. The results showed that tabu search and adaptive scale out shortens the delay for processing queries and improve Quality of Experience (QoE) of service users. This shows that the proposed flexible and in-situ resource provisioning scheme based on the demand for services is effective in providing regional IoT services. In future work, we update/extend the problems and models that consider more various constraints such as specific data, task (collecting, processing, aggregating task) and device type (e.g. sensors and actuators). We also evaluate the performance through more realistic simulation and a real-world testbed. Acknowledgement This work was supported in part by JSPS KAKENHI Grant Numbers 17J10021,16H01721 and 26220001. REFERENCES
Publisher Copyright:
© 2018 IEEE.
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.
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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.
T2 - 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018
Y2 - 25 October 2018 through 27 October 2018
ER -