PlaceRAN: optimal placement of virtualized network functions in Beyond 5G radio access networks

Morais, Fernando Zanferrari; Almeida, Gabriel Matheus Faria De; Pinto, Leizer de Lima; Cardoso, Kleber Vieira; Contreras, Luis Marcel Valenzuela; Righi, Rodrigo da Rosa; Both, Cristiano Bonato

doi:10.1109/tmc.2022.3171525

Cited by 50 publications

(31 citation statements)

References 30 publications

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“…The number of preaggregation rings is equal to the ratio between the total number of FAs and the number of FAs per pre-aggregation ring (rounded up to the next integer). The number of CPU cores required for baseband processing is as in [16] . Placing computing servers for baseband processing at the MA leads to 15% and 50% less server CapEx and OpEx, respectively, compared to when the same computing servers are placed at the cell site [4] .…”

Section: Resultsmentioning

confidence: 99%

Fiber- vs. Microwave-based 5G Transport: a Total Cost of Ownership Analysis

Lashgari¹,

Tonini²,

Capacchione³

et al. 2022

Preprint

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Section: Resultsmentioning

confidence: 99%

Fiber- vs. Microwave-based 5G Transport: a Total Cost of Ownership Analysis

Lashgari¹,

Tonini²,

Capacchione³

et al. 2022

Preprint

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“…Then, the work in [19] has proposed an optimized multi-cloud vRAN framework with balancing its centralization [3]. The authors in [20] have proposed the PlaceRAN framework to minimize the computing resources while maximizing the radio function aggregations using the IBM CPLEX solver. These mentioned works [3], [15]- [20] have addressed various optimization problems in vRANs.…”

Section: A Related Workmentioning

confidence: 99%

“…The authors in [20] have proposed the PlaceRAN framework to minimize the computing resources while maximizing the radio function aggregations using the IBM CPLEX solver. These mentioned works [3], [15]- [20] have addressed various optimization problems in vRANs. However, their frameworks require assuming complete models of the underlying system to define their problem structures and solve the problems.…”

Section: A Related Workmentioning

confidence: 99%

Constrained Deep Reinforcement Based Functional Split Optimization in Virtualized RANs

Murti

Ali

2022

IEEE Trans. Wireless Commun.

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In virtualized radio access network (vRAN), the base station (BS) functions are decomposed into virtualized components that can be hosted at the centralized unit or distributed units through functional splits. Such flexibility has many benefits; however, it also requires solving the problem of finding the optimal splits of functions of the BSs in such a way that minimizes the total network cost. The underlying vRAN system is complex and precise modelling of it is not trivial. Formulating the functional split problem to minimize the cost results in a combinatorial problem that is provably NP-hard, and solving it is computationally expensive. In this paper, a constrained deep reinforcement learning (RL) approach is proposed to solve the problem with minimal assumptions about the underlying system. Since in deep RL, the action selection is the outcome of inference of a neural network, it can be done in real-time while training to update the neural networks can be done in the background. However, since the problem is combinatorial, even for a small number of functions, the action space of the RL problem becomes large. Therefore, to deal with such a large action space, a chain rule-based stochastic policy is exploited in which a long short-term memory (LSTM) network-based sequence-to-sequence model is applied to estimate the policy that is selecting the functional split actions. However, the utilized policy is still limited to an unconstrained problem, and each split decision is bounded by vRAN's constraint requirements. Hence, a constrained policy gradient method is leveraged to train and guide the policy toward constraint satisfaction. Further, a search strategy by greedy decoding or temperature sampling is utilized to improve the optimality performance at the test time. Simulations are performed to evaluate the performance of the proposed solution using synthetic and real network datasets. Our numerical results show that the proposed RL solution architecture successfully learns to make optimal functional split decisions with the accuracy of the solution is up to 0.05% of the optimality gap. Moreover, our solution can achieve considerable cost savings compared to C-RAN or D-RAN systems and a faster computational time than the optimal baseline.

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“…A emulac ¸ão da rede de núcleo é feita pela ferramenta Free5GC, uma das primeiras implementac ¸ões de código aberto de um núcleo 5G baseado em servic ¸o e que implementa a especificac ¸ão da Realease 15 definido pelo 3GPP [Free5GC 2021]. Ambas ferramentas são amplamente utilizadas em estudos empíricos envolvendo o 5G [Tan et al 2020,Kaltenberger et al 2020,Morais et al 2021. Além disso, o OpenAirInterface opera com desagregac ¸ão da RAN.…”

Section: Observabilidade Em Sistemas 5gunclassified

“…Além disso, o OpenAirInterface opera com desagregac ¸ão da RAN. Para o posicionamento e implantac ¸ão das func ¸ões de rede do OpenAirInterface (i.e., RU, DU e CU) nos nós do cluster, a soluc ¸ão OPlaceRAN [Morais et al 2021] foi utilizada. Essa soluc ¸ão é capaz de implantar, de forma dinâmica e automática, as func ¸ões de rádio que compõem a RAN em Pods do K8S, tendo como entrada as características da topologia da rede.…”

Section: Observabilidade Em Sistemas 5gunclassified

Uma Investigação Empírica sobre Observabilidade em Sistemas 5G Nativos de Nuvem

Rodrigues

Bruno²,

Cardoso³

et al. 2022

Anais Do XL Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2022)

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Ao migrar sofisticados recursos de hardware para componentes de software executados em sistemas de nuvem, as redes 5G proveem grande flexibilidade. No entanto, a softwarização das redes 5G impõe grandes desafios à gerência da rede, especialmente em relação à corretude do sistema em tempo de execução. Neste contexto, se torna essencial que o comportamento do sistema seja facilmente observável. Este trabalho apresenta uma investigação empírica sobre observabilidade em um sistema 5G formado pelas redes de núcleo e de acesso. Resultados mostram que a análise combinada de métricas e logs melhora a observabilidade do sistema e facilita a antecipação de ocorrências de falhas. Nossa investigação demonstra também que, apesar de úteis, as mensagens de logs geradas pelo sistema 5G implantado são pouco estruturadas e pouco inteligíveis.

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PlaceRAN: optimal placement of virtualized network functions in Beyond 5G radio access networks

Cited by 50 publications

References 30 publications

Fiber- vs. Microwave-based 5G Transport: a Total Cost of Ownership Analysis

Fiber- vs. Microwave-based 5G Transport: a Total Cost of Ownership Analysis

Constrained Deep Reinforcement Based Functional Split Optimization in Virtualized RANs

Uma Investigação Empírica sobre Observabilidade em Sistemas 5G Nativos de Nuvem

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