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Real-time Optimization of Testbeds for Cloudified Radio Access Networks Using Artificial Intelligence

Fulltext:


Authors:

Animesh Singh , Chen Song , Jiecong Yang , Sahar Tahvili

Publication Type:

Conference/Workshop Paper

Venue:

The 2024 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications


Abstract

The evolution towards cloudification in Radio Access Networks (RAN) is transforming the telecommunications industry. To validate and assess the performance of Cloudified Radio Access Networks (C-RAN) applications, deploying cloud-native infrastructures in the form of test environments, testbeds, and test infrastructures becomes imperative. However, the intricacies and expenses associated with these testbeds surpass those of traditional testing environments. Effectively utilizing the potential of cloud-native testbeds necessitates real-time decision-making on multiple criteria, including compatibility, capability, cost, capacity, and availability. This paper introduces an Artificial Intelligence-based expert system designed to automatically schedule C-RAN testbeds. The proposed expert system is designed to consider and balance various factors in real-time, ensuring optimal utilization of resources and test infrastructures. Employing AI-based solutions optimizes scheduling decisions, adapts to dynamic environments, maximizes resource utilization, reduces operational costs, and improves turnaround times by dynamically adjusting priorities based on real-time conditions. The feasibility of the AI-based solution proposed in this paper is rigorously assessed through an empirical evaluation conducted on a Telecom use case at Ericsson AB in Sweden. The results demonstrate a remarkable 17% optimization in overall costs with the implementation of the proposed solution.

Bibtex

@inproceedings{Singh6953,
author = {Animesh Singh and Chen Song and Jiecong Yang and Sahar Tahvili},
title = {Real-time Optimization of Testbeds for Cloudified Radio Access Networks Using Artificial Intelligence},
month = {June},
year = {2024},
booktitle = {The 2024 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications},
url = {http://www.ipr.mdu.se/publications/6953-}
}