Minimizing Power Consumption by Joint Radio and Computing Resource Allocation in Cloud-Ran

Sharara, Mahdi; Hoteit, Sahar; Bassi, Francesca

doi:10.1109/iscc55528.2022.9912943

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…To the best of our knowledge, In the current literature, there is no explicit comparison of joint vs. sequential resource allocation. Furthermore, the advantages, limitations, and influence of the This paper is an extension of our work in [27]. While [27] only models the joint MILP problem, which is NP-hard, and shows the benefits of having a joint radio and computing resources allocation, this extension provides a lowcomplexity alternative to the MILP problem.…”

Section: Matching Gamesmentioning

confidence: 99%

“…Furthermore, the advantages, limitations, and influence of the This paper is an extension of our work in [27]. While [27] only models the joint MILP problem, which is NP-hard, and shows the benefits of having a joint radio and computing resources allocation, this extension provides a lowcomplexity alternative to the MILP problem. It is based on matching theory, and it yields solutions that are close to the optimal MILP problem solutions.…”

Section: Matching Gamesmentioning

confidence: 99%

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Minimizing Energy Consumption by Joint Radio and Computing Resource Allocation in Cloud-Ran

Sharara

Fossati

Hoteit³

et al. 2023

Preprint

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Section: Matching Gamesmentioning

confidence: 99%

Section: Matching Gamesmentioning

confidence: 99%

Minimizing Energy Consumption by Joint Radio and Computing Resource Allocation in Cloud-Ran

Sharara

Fossati

Hoteit³

et al. 2023

Preprint

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“…( 8),( 9), (10), (11) together enforce this condition. They use the auxiliary binary variable z c defined in (12). M is the big-M notation.…”

Section: Context and Problem Formulationmentioning

confidence: 99%

“…In [10], computing resources are allocated using Integer Linear Programming (ILP) to maximize throughput and fairness, while [11] proposes a lowcomplexity ML-based alternative for these two objectives. In [12], the radio and computing resources allocation aims to minimize power consumption, reducing operators' OPEX.…”

Section: Introductionmentioning

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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