Cost-Aware Placement Optimization of Edge Servers for IoT Services in Wireless Metropolitan Area Networks

Edge computing migrates cloud computing capacity to the edge of the network to reduce latency caused by congestion and long propagation distance of the core network. And the Internet of things (IoT) service requests with large data traffic submitted by users need to be processed quickly by correspon...

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Veröffentlicht in:	Wireless communications and mobile computing Jg. 2022; H. 1
Hauptverfasser:	Shao, Yanling, Shen, Zhen, Gong, Siliang, Huang, Hanyao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Hindawi 2022 John Wiley & Sons, Inc
Schlagworte:	Algorithms Bending machines Cloud computing Collaboration Communication Communications traffic Construction costs Costs Density Edge computing Heuristic Integer programming Internet Internet of Things Linear programming Metropolitan area networks Metropolitan areas Mixed integer Network latency Nodes Nonlinear programming Optimization Optimization algorithms Placement Resource allocation Resource utilization Servers User experience User requirements Wireless networks Workloads
ISSN:	1530-8669, 1530-8677
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Abstract	Edge computing migrates cloud computing capacity to the edge of the network to reduce latency caused by congestion and long propagation distance of the core network. And the Internet of things (IoT) service requests with large data traffic submitted by users need to be processed quickly by corresponding edge servers. The closer the edge computing resources are to the user network access point, the better the user experience can be improved. On the other hand, the closer the edge server is to users, the fewer users will access simultaneously, and the utilization efficiency of nodes will be reduced. The capital investment cost is limited for edge resource providers, so the deployment of edge servers needs to consider the trade-off between user experience and capital investment cost. In our study, for edge server deployment problems, we summarize three critical issues: edge location, user association, and capacity at edge locations through the research and analysis of edge resource allocation in a real edge computing environment. For these issues, this study considers the user distribution density (load density), determines a reasonable deployment location of edge servers, and deploys an appropriate number of edge computing nodes in this location to improve resource utilization and minimize the deployment cost of edge servers. Based on the objective minimization function of construction cost and total access delay cost, we formulate the edge server placement as a mixed-integer nonlinear programming problem (MINP) and then propose an edge server deployment optimization algorithm to seek the optimal solution (named Benders_SD). Extensive simulations and comparisons with the other three existing deployment methods show that our proposed method achieved an intended performance. It not only meets the low latency requirements of users but also reduces the deployment cost.
AbstractList	Edge computing migrates cloud computing capacity to the edge of the network to reduce latency caused by congestion and long propagation distance of the core network. And the Internet of things (IoT) service requests with large data traffic submitted by users need to be processed quickly by corresponding edge servers. The closer the edge computing resources are to the user network access point, the better the user experience can be improved. On the other hand, the closer the edge server is to users, the fewer users will access simultaneously, and the utilization efficiency of nodes will be reduced. The capital investment cost is limited for edge resource providers, so the deployment of edge servers needs to consider the trade‐off between user experience and capital investment cost. In our study, for edge server deployment problems, we summarize three critical issues: edge location, user association, and capacity at edge locations through the research and analysis of edge resource allocation in a real edge computing environment. For these issues, this study considers the user distribution density (load density), determines a reasonable deployment location of edge servers, and deploys an appropriate number of edge computing nodes in this location to improve resource utilization and minimize the deployment cost of edge servers. Based on the objective minimization function of construction cost and total access delay cost, we formulate the edge server placement as a mixed‐integer nonlinear programming problem (MINP) and then propose an edge server deployment optimization algorithm to seek the optimal solution (named Benders_SD). Extensive simulations and comparisons with the other three existing deployment methods show that our proposed method achieved an intended performance. It not only meets the low latency requirements of users but also reduces the deployment cost.
Author	Shen, Zhen Shao, Yanling Gong, Siliang Huang, Hanyao
Author_xml	– sequence: 1 givenname: Yanling orcidid: 0000-0003-1276-5994 surname: Shao fullname: Shao, Yanling organization: School of Computer and softwareNanyang Institute of TechnologyNanyang 473000Chinanyist.edu.cn – sequence: 2 givenname: Zhen surname: Shen fullname: Shen, Zhen organization: School of Computer and softwareNanyang Institute of TechnologyNanyang 473000Chinanyist.edu.cn – sequence: 3 givenname: Siliang surname: Gong fullname: Gong, Siliang organization: School of Computer and softwareNanyang Institute of TechnologyNanyang 473000Chinanyist.edu.cn – sequence: 4 givenname: Hanyao surname: Huang fullname: Huang, Hanyao organization: School of Computer and softwareNanyang Institute of TechnologyNanyang 473000Chinanyist.edu.cn
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CitedBy_id	crossref_primary_10_1038_s41598_024_58048_0 crossref_primary_10_1016_j_cosrev_2023_100616 crossref_primary_10_1016_j_iot_2024_101121 crossref_primary_10_1007_s11280_024_01273_4 crossref_primary_10_1186_s13677_024_00709_6
Cites_doi	10.1287/opre.1060.0371 10.1007/978-3-319-15509-8_27 10.1109/TPDS.2015.2510638 10.1109/90.958323 10.1109/TPDS.2016.2604803 10.1109/TITS.2021.3091321 10.1016/j.jpdc.2018.06.008 10.1016/j.jpdc.2021.03.007 10.1109/GLOCOM.2016.7841576 10.1007/s10586-014-0365-0 10.1109/JSEN.2021.3060953 10.1109/JIOT.2020.3041805 10.1007/978-981-15-1899-7_10 10.1109/ICC.2017.7996722 10.1109/TMC.2021.3136868 10.1109/TCC.2015.2449834 10.1155/2021/6455617 10.1016/j.comnet.2021.108393 10.1109/JSEN.2020.3033526 10.1016/j.pmcj.2020.101312 10.1109/TITS.2021.3059455 10.1016/j.asoc.2021.107108 10.1016/j.cor.2003.11.012 10.1109/TAES.2022.3169130 10.1002/spe.2685 10.1049/iet-com.2020.0651 10.1109/CCGRID.2017.34
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Copyright	Copyright © 2022 Yanling Shao et al. Copyright © 2022 Yanling Shao et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms	Algorithms Bending machines Cloud computing Collaboration Communication Communications traffic Construction costs Costs Density Edge computing Heuristic Integer programming Internet Internet of Things Linear programming Metropolitan area networks Metropolitan areas Mixed integer Network latency Nodes Nonlinear programming Optimization Optimization algorithms Placement Resource allocation Resource utilization Servers User experience User requirements Wireless networks Workloads
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Title	Cost-Aware Placement Optimization of Edge Servers for IoT Services in Wireless Metropolitan Area Networks
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