You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at http://www.ieee.org/web/publications/rights/copyrightpolicy.html)
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at http://www.acm.org/pubs/copyright_policy/)
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact webmaster@ide.mdh.se

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

Authors:

Animesh Singh , Chen Song , Jiecong Yang , Sahar Tahvili

Research groups:

Software Testing Laboratory
Industrial AI Systems

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.es.mdu.se/publications/6953-}
}