Priority-Aware Service Orchestration Using Big Data Analytics for Dynamic Slicing in 5G Transport Networks

Raza, Muhammad; Rostami, A.; Vidal, Allan; Santos, Mateus Augusto Silva; Wosinska, Lena; Monti, Paolo

doi:10.1109/ecoc.2017.8346133

Cited by 8 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar approach is experimentally demonstrated by the work in [6]. The authors in [7] aim at minimizing degradation and show how BDA predictions can help while scaling up/down slices with different priorities. On the other hand, there is not much work available in the literature related to RL-based network slicing methods.…”

Section: Network Slicing and Machine Learningmentioning

confidence: 79%

Machine Learning Methods for Slice Admission in 5G Networks

Raza

Natalino

Wosinska

et al. 2019

2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and

View full text Add to dashboard Cite

The paper discusses how the slice admission problem can be aided by machine learning strategies. Results show that both supervised and reinforcement learning might lead to profit maximization while containing losses due to performance degradation.Keywords: Data analytics for network control and management in optical core/data center networks; Optical core/metro/data-center network architecture, design, virtualization, slice, control and management

show abstract

Section: Network Slicing and Machine Learningmentioning

confidence: 79%

Machine Learning Methods for Slice Admission in 5G Networks

Raza

Natalino

Wosinska

et al. 2019

2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and

View full text Add to dashboard Cite

show abstract

“…Each RDC has a capacity g r = 80, each CO g c = 50 and each link d l = 50. The reference behavior of slices (profiles) are the ones reported by Raza et al [11]. The resource requirements of each slice are given by the time of the day and the type of the slice.…”

Section: Discussionmentioning

confidence: 99%

Tenant-Aware Slice Admission Control Using Neural Networks-Based Policy Agent

Batista

Khan

Öhlen

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

5G networks will provide the platform for deploying large number of tenant-associated management, control and end-user applications having different resource requirements at the infrastructure level. In this context, the 5G infrastructure provider must optimize the infrastructure resource utilization and increase its revenue by intelligently admitting network slices that bring the most revenue to the system. In addition, it must ensure that resources can be scaled dynamically for the deployed slices when there is a demand for them from the deployed slices. In this paper, we present a neural networks-driven policy agent for network slice admission that learns the characteristics of the slices deployed by the network tenants from their resource requirements profile and balances the costs and benefits of slice admission against resource management and orchestration costs. The policy agent learns to admit the most profitable slices in the network while ensuring their resource demands can be scaled elastically. We present the system model, the policy agent architecture and results from simulation study showing an increased revenue for infra-structure provider compared to other relevant slice admission strategies.

show abstract