Three-Tier Capacity and Traffic Allocation for Core, Edges, and Devices for Mobile Edge Computing

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dc.contributor.author Lin, Ying-Dar
dc.contributor.author Lai, Yuan-Cheng
dc.contributor.author Huang, Jian-Xun
dc.contributor.author Chien, Hsu-Tung
dc.date.accessioned 2019-04-05T07:46:29Z
dc.date.available 2019-04-05T07:46:29Z
dc.date.issued 2018-07-03
dc.identifier.bibliographicCitation Lin, Y-D., Lai, Y-C., Huang, J-X y Chien, H-T. (2018). Three-Tier Capacity and Traffic Allocation for Core, Edges, and Devices for Mobile Edge Computing. IEEE Transactions on Network and Service Management, 15 (3), pp. 923-933
dc.identifier.issn 1932-4537
dc.identifier.uri http://hdl.handle.net/10016/28282
dc.description.abstract In order to satisfy the 5G requirements of ultra-low latency, mobile edge computing (MEC)-based architecture, composed of three-tier nodes, core, edges, and devices, is proposed. In MEC-based architecture, previous studies focused on the controlplane issue, i.e., how to allocate traffic to be processed at different nodes to meet this ultra-low latency requirement. Also important is how to allocate the capacity to different nodes in the management plane so as to establish a minimal-capacity network. The objectives of this paper is to solve two problems: 1) to allocate the capacity of all nodes in MEC-based architecture so as to provide a minimal-capacity network and 2) to allocate the traffic to satisfy the latency percentage constraint, i.e., at least a percentage of traffic satisfying the latency constraint. In order to achieve these objectives, a two-phase iterative optimization (TPIO) method is proposed to try to optimize capacity and traffic allocation in MEC-based architecture. TPIO iteratively uses two phases to adjust capacity and traffic allocation respectively because they are tightly coupled. In the first phase, using queuing theory calculates the optimal traffic allocation under fixed allocated capacity, while in the second phase, allocated capacity is further reduced under fixed traffic allocation to satisfy the latency percentage constraint. Simulation results show that MEC-based architecture can save about 20.7% of capacity of two-tier architecture. Further, an extra 12.2% capacity must be forfeited when the percentage of satisfying latency is 90%, compared to 50%.
dc.description.sponsorship This work was supported in part by H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant number 761586), and Ministry of Science and Technology, Taiwan for financially supporting this research under Contract No. MOST 106-2218-E-009-018.
dc.format.extent 11
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2018 IEEE
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Iterative optimization
dc.subject.other Mobile edge computing (MEC)
dc.subject.other Three-tier architecture
dc.subject.other Capacity allocation
dc.title Three-Tier Capacity and Traffic Allocation for Core, Edges, and Devices for Mobile Edge Computing
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://www.doi.org/10.1109/TNSM.2018.2852643
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/761586
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 923
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 933
dc.identifier.publicationtitle IEEE Transactions on Network and Service Management
dc.identifier.publicationvolume 15
dc.contributor.funder European Commission
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