Machine Learning Enabling Traffic-Aware Dynamic Slicing for 5G Optical Transport Networks

Song, Chaosheng; Zhang, Min; Huang, Xuetian; Zhan, Yueying; Wang, Danshi; Rong, Yanhong

doi:10.1364/cleo_at.2018.jtu2a.44

Cited by 17 publications

(10 citation statements)

References 1 publication

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“…Chuang Song et al [52] describe a dynamic traffic slice model based on ML (ML-TADS). This model allows to manage traffic in the network competently -to provide it in such a way that its distribution is uniform, there is no congestion at one BS, and, at the same time, zero traffic to another.…”

Section: F Network Traffic Predictionmentioning

confidence: 99%

Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network

Alekseeva¹,

Stepanov

Veprev

et al. 2021

IEEE Access

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Section: F Network Traffic Predictionmentioning

confidence: 99%

Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network

Alekseeva¹,

Stepanov

Veprev

et al. 2021

IEEE Access

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“…The transport network consist of high bandwidth optical cores [24], and can be physically sliced or virtually sliced. Physical slicing involves allocating each fiber core to a single tenant while virtual slicing is where more than one tenant share the same core.…”

Section: E Slicing Domain:end-to-end Ran Core and Cloud Slicingmentioning

confidence: 99%

A Survey on Slice Admission Control Strategies and Optimization Schemes in 5G Network

Ojijo

Falowo

2020

IEEE Access

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The Fifth Generation(5G) communication network is envisioned to provide heterogeneous services tailored to specific user demands. These services are diverse and can be generally categorized based on latency, bandwidth, reliability, and connection density requirements. The 5G infrastructure providers are expected to employ network function virtualization, software-defined networking, and network slicing for cost-effective and efficient network resource allocation. In the 5G network, when an infrastructure provider receives a slice request, a slice admission control scheme is applied and an optimization algorithm is used to achieve predefined objectives. To this end, a number of slice admission control objectives, strategies and algorithms have been proposed. However, there is a need to present a coherent review and bridge the gap between many aspects of slice admission control. In this paper, we present the latest developments in this research area. Thus, we begin by introducing slice admission control and discuss background concepts associated with slicing. We then extend our discussion to slice admission objectives followed by the strategies and optimization algorithms. Finally, we conclude with a summary of analysis containing the optimization algorithms.

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“…Access, core, Transport, Backhauls) entails slicing in both links and node resources. However, the management of link resources is a more critical part of the network slicing and presents new research challenges to be addressed (e.g., bandwidth allocation along a path, management of the prioritization on the links, and isolation between the slices in terms of traffic) compared to node resources [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Squatting and kicking model evaluation for prioritized sliced resource management

2020

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Machine Learning Enabling Traffic-Aware Dynamic Slicing for 5G Optical Transport Networks

Cited by 17 publications

References 1 publication

Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network

Comparison of Machine Learning Techniques Applied to Traffic Prediction of Real Wireless Network

A Survey on Slice Admission Control Strategies and Optimization Schemes in 5G Network

Squatting and kicking model evaluation for prioritized sliced resource management

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