AI-assisted resource advertising and pricing to realize distributed tenant-driven virtual network slicing in inter-DC optical networks

Lü, Wei; Fang, Hongqiang; Zhu, Zuqing

doi:10.23919/ondm.2018.8396119

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Similarly, the authors of [169] establish their research work based on the same O-DCIs reference architecture as [168] to maximize the InP's overall profits. However, in this case, the InP only relies on the DQN agent to generate the resource pricing/advertising and map the VNE requirements from MVNOs.…”

Section: ) Resource Allocation In Tnmentioning

confidence: 99%

Machine Learning in Network Slicing—A Survey

Phyu¹,

Naboulsi²,

Stanica

2023

IEEE Access

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5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional "one-size-fits-all" network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances in machine learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and subcategories in the literature. Lessons learned are also presented and open research challenges are discussed, together with potential solutions.

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Section: ) Resource Allocation In Tnmentioning

confidence: 99%

Machine Learning in Network Slicing—A Survey

Phyu¹,

Naboulsi²,

Stanica

2023

IEEE Access

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“…This method is utilized by the authors for the analysis of error signals in an earlier research 29 . The authors have used deep reinforcement learning in previous studies 30,31 …”

Section: Related Workmentioning

confidence: 99%

“…29 The authors have used deep reinforcement learning in previous studies. 30,31 Despite a ton of applications of ML in optical communications, predicting performance is something very crucial for the realization of the next generation of cognitive/self-learning optical networks. However, due to complex interaction of multiple parameters and linear/nonlinear impairments, predicting the performance using analytical models often seems to be a daunting task.…”

Section: Related Workmentioning

confidence: 99%

Machine learning‐based predictive modeling for failure management of optical spatial mode division multiplexing system

Kaur

Parasher²,

Srivastava

et al. 2022

Int J Communication

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The bit error rate (BER) in optical communication systems is often get degraded due to various factors like launch power, dispersion, modal noise, and so on. Finding the most optimal launch power for a signal to provide acceptable BER is usually difficult on an installed link. Therefore, in this paper, an attempt has been made to use machine learning-based linear regression technique for predicting the optimal signal quality for spatial division multiplexed (SDM)-based fiber optical transmission system for a fixed distance. This technique helps in predicting the optimal value of continuous launch power. Therefore, to demonstrate the given concept in this research work, the generic setup of an SDM optical long-haul system of 20 km in length has been designed and simulated in Optisystem-14.0. The light sources in our experiments are two spatial optical transmitters with an emission wavelength of around 1550 nm. Abiding to the first step for the regression analysis, that is the data preparation, normality and correlation checks were performed. After this, a linear regression model is developed which was validated through a summary report, coefficients and diagnostic plots. Furthermore, the accuracy of the model is improved by employing Cook's distance. These tests help in dealing with the influence points that hinders the prediction ability of the proposed model. The results show that the R-squared value is 0.9366, and value of adjusted R 2 comes out to be around 0.9344; we therefore came to infer that the model explains nearly 94% variations in the dependent variables. K E Y W O R D Sbit error rate (BER), launch power (LP), linear regression, optical transmission systems, spatial division multiplex (SDM)

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“…Similarly, the authors of [191] establish their research work based on the same O-DCIs reference architecture as [187] to maximize the InP's overall profits. However, in this case, the InP only relies on the DQN agent to generate the resource pricing/advertising and map the VNE requirements from MVNOs.…”

Section: ) Resource Allocation In Tnmentioning

confidence: 99%

Machine Learning in Network Slicing - A Survey

Phyu¹,

Naboulsi²,

Stanica³

2022

Preprint

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<p>5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous<br> requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances on Machine Learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources, in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on<br> ML in network slicing, identifying major categories and sub-categories in the literature. Key takeaways are also presented and open research challenges are discussed, together with potential solutions.</p>

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AI-assisted resource advertising and pricing to realize distributed tenant-driven virtual network slicing in inter-DC optical networks

Cited by 5 publications

References 15 publications

Machine Learning in Network Slicing—A Survey

Machine Learning in Network Slicing—A Survey

Machine learning‐based predictive modeling for failure management of optical spatial mode division multiplexing system

Machine Learning in Network Slicing - A Survey

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