Latency-aware cost optimization of the service infrastructure placement in 5G networks

Santoyo-González, Alejandro; Cervelló-Pastor, Cristina

doi:10.1016/j.jnca.2018.04.007

Cited by 52 publications

(47 citation statements)

References 22 publications

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“…In References [22,23], the authors present optimal and suboptimal strategies for placement of edge resources in 5G networks. In Reference [22], a framework to optimize the placement of primary and backup 5G user plane functions (UPFs) at the edge is provided.…”

Section: Introductionmentioning

confidence: 99%

“…However, the proposed model considers only backhaul links and do not account for the finite optical link capacity, which may affect the solution, especially when dealing with very high bitrate requirements of Fronthaul links. The model in Reference [23], instead, focuses on the number of edge nodes to be equipped with computational capacity, which is shown to increase with the number of base stations deployed in the area.…”

Section: Introductionmentioning

confidence: 99%

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Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Tonini

Khorsandi

Amato

et al. 2019

JSAN

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The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Tonini

Khorsandi

Amato

et al. 2019

JSAN

View full text Add to dashboard Cite

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“…To model more closely real network scenarios, we assume that only a discrete set of capacity values can be chosen by the network operator and made available. Therefore, we adopt a piecewise-constant function S i for the processing capacity of an edge node, in line with [6]. This is defined as:…”

Section: Network Planning and Routing Issuesmentioning

confidence: 99%

“…The authors in [12] propose a mixed integer linear programming (MILP) model to study cloudlet placement, assignment of access points (APs) to cloudlets and traffic routing problems, by minimizing installation costs of network facilities. The work in [6] proposes a MILP model for the problem of fog nodes placement under capacity and latency constraints.…”

Section: Network Planningmentioning

confidence: 99%

Joint Network Slicing and Mobile Edge Computing in 5G Networks

Xiang

Elias

Martignon

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. Our paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.

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“…To decrease the EN network costs, we aim to optimize the site selection and reduce the number of ENs deployed and the capacity assigned to each EN. The underlying problem formulation models the service demand, distributed over a given area, as traffic generators (TGs) [23]. In more detail, the aggregated cell structure composed by the TGs in the form of 4G/5G mobile base stations, wireless access points, etc., is considered to be known.…”

Section: Framework Proposalmentioning

confidence: 99%

A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G

Leyva-Pupo

Santoyo-González

Cervelló-Pastor

2019

Sensors

Self Cite

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Achieving less than 1 ms end-to-end communication latency, required for certain 5G services and use cases, is imposing severe technical challenges for the deployment of next-generation networks. To achieve such an ambitious goal, the service infrastructure and User Plane Function (UPF) placement at the network edge, is mandatory. However, this solution implies a substantial increase in deployment and operational costs. To cost-effectively solve this joint placement problem, this paper introduces a framework to jointly address the placement of edge nodes (ENs) and UPFs. Our framework proposal relies on Integer Linear Programming (ILP) and heuristic solutions. The main objective is to determine the ENs and UPFs’ optimal number and locations to minimize overall costs while satisfying the service requirements. To this aim, several parameters and factors are considered, such as capacity, latency, costs and site restrictions. The proposed solutions are evaluated based on different metrics and the obtained results showcase over 20 % cost savings for the service infrastructure deployment. Moreover, the gap between the UPF placement heuristic and the optimal solution is equal to only one UPF in the worst cases, and a computation time reduction of over 35 % is achieved in all the use cases studied.

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Latency-aware cost optimization of the service infrastructure placement in 5G networks

Cited by 52 publications

References 22 publications

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

Joint Network Slicing and Mobile Edge Computing in 5G Networks

A Framework for the Joint Placement of Edge Service Infrastructure and User Plane Functions for 5G

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