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Dataset Open Access

Results of the MECinFire experiment in Fed4FIRE+ EU project

Apostolaras Apostolis

Multi-access Edge Computing (MEC) has been proposed as the means to drastically minimize the service access latency, by bringing computational resources and services closer to the wireless network edge. Edge resources are planned to be extensively used in the 5G network deployments, as they are able to meet stiff latency demands required from services being developed around this ecosystem (e.g. AR/VR, e-Health, Industry 4.0, etc.), by providing computational capabilities where such services can be executed close to the network edge. At the same time, 5G networks redefine the operation of traditional base station units, by disaggregating them and operating part of them in the Cloud, thus creating Cloud-RANs. These Cloud-RANs can also be heterogeneous, allowing users to access the network through multiple wireless technologies (e.g. dual access through 5G-NR and LTE). In this project, we blend the novel disaggregated and heterogeneous base station architecture with the MEC concept, and develop and experiment with the deployment of the edge computing services even closer to the network edge. In MECinFIRE we developed a software prototype that allows services to be executed close or over the machines hosting the radio access services for the network access.  Our experiment provides several proof-of-concept experiments that illustrate the applicability and benefits of our solution in real 5G networks. The experiment was evaluated in a real testbed environment, while measuring KPIs regarding the end-to-end user to service latency.

This repository contains the dataset of the experimental results produced by the MECinFIRE Project within the FED4FIRE+.

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