Maximizing Infrastructure Providers’ Revenue Through Network Slicing in 5G

Vincenzi, Matteo; López-Aguilera, Elena; García-Villegas, Eduard

doi:10.1109/access.2019.2939935

Cited by 26 publications

(43 citation statements)

References 15 publications

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“…Indeed, if research and standardization efforts initially focused on architectures and enabling technologies, 5G ecosystem's drive is becoming progressively service-oriented. On the one hand, manufacturers and network owners are willing to fully exploit the potential of 5G's marketplace, on the other hand, regulation authorities and standardization bodies implement solutions for the healthy coexistence among parties [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Timely Admission Control for Network Slicing in 5G With Machine Learning

2021

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For guaranteeing the strict requirements foreseen for 5G, network slicing has been proposed as a dynamic and scalable mechanism for the allocation of customized resources to service providers. Many solutions have been proposed in the literature for the scenario where multiple service providers share the same pool of resources, while the exclusive allocation to different providers is still an open issue due to the associated complexity. In this work, we define a policy-based admission mechanism for exclusive intraservice slice allocation, at fine and adaptable timescales. In particular, we consider the case where optimal admission strategies are pre-computed offline for network state conditions that are representative of typical traffic loads and resource availability. This offline phase is also used to train a Machine learning algorithm; a neural network (NN) learns the best admission policies from a more computationally expensive mechanism in previously studied network conditions. Thus, the NN is used for providing near-optimal admission decisions at runtime under network conditions for which no optimal policy has been computed. The potential of the 5G marketplace in terms of revenue and quality of service is demonstrated for the particular case of services with strict latency constraints by means of a proof of concept tested over network traces from a real network operator. Different strategies are compared for the computation of the admission strategies and results are provided in terms of efficiency in resource utilization, fairness to the service providers, network owners' revenue and complexity. This study confirms the feasibility of a policy-based approach for exclusive intra-service resource allocation, especially if computational-efficient mechanisms are adopted in the case of missing information about network states.INDEX TERMS 5G networks, mobile networks, network slicing, admission control, machine learning, neural networks, clustering, Markov processes, pricing.

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Section: Introductionmentioning

confidence: 99%

Timely Admission Control for Network Slicing in 5G With Machine Learning

2021

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“…The issue of pricing for services was considered in [18], and such an optimization structure allows us to find a compromise between the interests of communication service providers and the social welfare of the network, without violating the interests of users. Maximizing revenue using the access control mechanism for the network slice is presented in [16]. Infrastructure providers have the ability to rent network slices, both one-time and on a periodic basis.…”

Section: Introductionmentioning

confidence: 99%

Resource Queueing System for Analysis of Network Slicing Performance with QoS-Based Isolation

Moskaleva

Lisovskaya

Gaidamaka

2021

Information Technologies and Mathematical Modelling. Queueing Theory and Applications

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Network slicing is defined as one of the main components of fifth-generation mobile communications that can solve the problem of colossal growth in data volume traffic in cellular networks. A key feature of slicing is to limit the effect of one slice on another to provide a high quality of service. Therefore, in this paper, a model for resource sharing in slicing using the queueing theory methods is constructed. The main aim is to determine how radio resources should be fairly shared between different slices in the system. The proposed algorithm ensures the isolation of slices according to the quality of service. The resource sharing problem is formulated as an optimization problem. Analysis of the system's performance characteristics will allow us to conclude that the isolation parameter has a significant effect on metrics of interest.

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“…Network slicing at the admission-control level is discussed in Reference [33] along with other approaches to RAN slicing, while user blocking probabilities for such systems are estimated in References [17,21]. Besides, since network slices are to be created and terminated dynamically [2], some studies [31,32,34] investigate the slice admission control, that is, whether a new slice instance can/should be admitted to the network. The problem of associating users to base stations under slicing constraints is addressed in References [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning along with Markov decision processes are applied in References [25,31,32]. Continuous-Time Markov Chain (CTMC) models for network slicing analysis are proposed in References [17,34]. The authors of Reference [34] consider a one-dimensional CTMC which transitions represent slice instantiation and termination, and use it to establish a flexible auction-based slice-admission policy.…”

Section: Introductionmentioning

confidence: 99%

“…Continuous-Time Markov Chain (CTMC) models for network slicing analysis are proposed in References [17,34]. The authors of Reference [34] consider a one-dimensional CTMC which transitions represent slice instantiation and termination, and use it to establish a flexible auction-based slice-admission policy. A four-dimensional CTMC which transitions correspond to establishing and terminating user sessions is proposed in Reference [17] to analyze the performance of resource allocation to two network slices each offering two GBR services with different priorities.…”

Section: Introductionmentioning

confidence: 99%

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An Analytical Model for 5G Network Resource Sharing with Flexible SLA-Oriented Slice Isolation

et al. 2020

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Network slicing is a novel key technology in 5G networks which permits to provide a multitude of heterogeneous communication services over a common network infrastructure while satisfying strict Quality of Service (QoS) requirements. Since radio spectrum resources are inherently scarce, the slicing of the radio access network should rely on a flexible resource sharing policy that provides efficient resource usage, fairness and slice isolation. In this article, we propose such a policy for bandwidth-greedy communication services. The policy implies a convex programming problem and is formalized to allow for session-level stochastic modeling. We developed a multi-class service system with service rates obtained as a solution to the optimization problem, a Markovian Arrival Process and state-dependent preemptive priorities. We use matrix-analytic methods to find the steady state distribution of the resulting continuous-time Markov chain and the expressions for important performance metrics, such as data rates. Numerical analysis illustrates the efficiency of the proposed slicing scheme compared to the complete sharing and complete partitioning policies, showing that our approach leads to a data rate about the double of that obtained under complete partitioning for the analyzed scenario.

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Maximizing Infrastructure Providers’ Revenue Through Network Slicing in 5G

Cited by 26 publications

References 15 publications

Timely Admission Control for Network Slicing in 5G With Machine Learning

Timely Admission Control for Network Slicing in 5G With Machine Learning

Resource Queueing System for Analysis of Network Slicing Performance with QoS-Based Isolation

An Analytical Model for 5G Network Resource Sharing with Flexible SLA-Oriented Slice Isolation

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