Joint fiber and MEC deployment for sparsely populated areas

The deployment of multi-access edge computing (MEC) networks gives rise to the MEC placement problem, which deals with finding the right server locations to reduce the cost and guarantee network performance. Multiple papers have been presented to solve this problem, but they are usually oriented to...

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Veröffentlicht in:Journal of optical communications and networking 2024-01, Vol.16 (1), p.45-58
Hauptverfasser: Anzola-Rojas, Camilo, de Miguel, Ignacio, Aguado, Juan Carlos, Merayo, Noemi, Fernandez, Patricia, Duran Barroso, Ramon J.
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Sprache:eng
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Zusammenfassung:The deployment of multi-access edge computing (MEC) networks gives rise to the MEC placement problem, which deals with finding the right server locations to reduce the cost and guarantee network performance. Multiple papers have been presented to solve this problem, but they are usually oriented to urban areas where short distances and high-quality network infrastructure are assumed. When this problem must be solved for sparsely populated areas, like rural environments, the connectivity is not always granted and the deployment of such connectivity using fiber technologies should be included in the problem. In contrast to urban areas, where the density of users is high and therefore the main problem is capacity, in sparsely populated areas, the problem lies in how to cost-effectively plan the MEC sites and the interconnecting network while meeting the delay constraints of the services offered through that network. This paper proposes a technique to solve the MEC placement problem considering the joint deployment of the optical network required to interconnect the base stations and the MEC servers. It consists of a three-phase scheme, which combines a spanning tree topology, for fiber deployment, with the use of mixed integer linear programming (MILP) formulations to minimize MEC servers and MEC data centers (MEC-DCs). We have applied the technique in a case study for a province in Spain (Valladolid, 8110\;{{\rm km}^2} ), obtaining a reduction of around 50% of the total cost when compared to a previous work. In addition, a clustering method is proposed to improve the scalability of the model for large scenarios. A simulation study is also presented to demonstrate the performance of the proposal assuming a {94}{,}{226}\;{{\rm km}^2} region (Castilla y León) with 1576 base stations.
ISSN:1943-0620
1943-0639
DOI:10.1364/JOCN.500429