Reducing operational costs of ultra-reliable low latency services in 5G

Varga, Jozsef; Hilt, Attila; Bı́ró, József; Rotter, Csaba; Járó, G.

doi:10.36244/icj.2018.4.6

Cited by 12 publications

(1 citation statement)

References 7 publications

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“…The O-DUs and O-CUs run as virtual machines in the Open Cloud (O-Cloud) and have to share the computing resources available at this O-Cloud, which is not owned by the operator. [13], [14] show that achieving high reliability and low latency requires deploying redundant hardware. However, we are only targeting the case where computing resources are not enough either due to bad provisioning or because of some financial restrictions for the operator.…”

Section: Context and Problem Formulationmentioning

confidence: 99%

Reinforcement Learning based model for Maximizing Operator's Profit in Open-RAN

Sharara

Hoteit

2023

NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium

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Open Radio Access Network (O-RAN) is a novel architecture that enables the disaggregation and the virtualization of network components. This would provide new ways to mix and match network components by "opening up" the interfaces between them. O-RAN enables driving down the costs of network deployments and allows the entry of new players into the RAN market. It enables network operators to maximize resource utilization and deliver new network edge services at a lower cost, resulting in higher profits for operators. In this context, we consider a computing resource allocation problem for maximizing the operator's profit. Given that an operator receives subscribers' payments and pays the infrastructure provider's costs, we model the problem using Mixed Integer Linear Programming (MILP). Then, we propose to solve the problem using Reinforcement Learning (RL). Our simulation results demonstrate the ability of the RL agent to increase the operator's profit while reducing the algorithmic complexity of the MILP solver.

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Section: Context and Problem Formulationmentioning

confidence: 99%