Network slicing is a key feature of forthcoming fifth generation (5G) systems to facilitate the partitioning of the network into multiple logical networks customized according to different business and application needs. Network slicing is a fundamental capability for enabling a cost-effective deployment and operation of 5G, as it allows the materialization of multi-tenant networks in which the same infrastructure is shared among multiple communication providers, each one using a different slice. This paper proposes a Markovian approach to characterize the resource sharing in multi-tenant scenarios with diverse guaranteed bit rate services by considering a slice-aware admission control policy. After describing the Markov model and its implementation and discussing its suitability, the model is applied to study the performance attained in a scenario with two different slices, one for enhanced mobile broadband communications and the other for mission critical services. The system is analyzed under standard and disaster situations, thus illustrating the capability to properly manage the different multi-tenant and multi-service traffic loads. INDEX TERMS Admission control, Markov processes, mobile communication, multi-tenancy, radio access networks, RAN slicing.
5G systems are envisaged to support a wide range of application scenarios with variate requirements. To handle this heterogeneity, 5G architecture includes network slicing capabilities that facilitate the partitioning of a single network infrastructure into multiple logical networks on top of it, each tailored to a given use case and provided with appropriate isolation and Quality of Service (QoS) characteristics. Network slicing also enables the use of multi-tenancy networks, in which the same infrastructure can be shared by multiple tenants by associating one slice to each tenant, easing the cost-effective deployment and operation of future 5G networks. Concerning the Radio Access Network (RAN), slicing is particularly challenging as it implies the configuration of multiple RAN behaviors over a common pool of radio resources. In this context, this work presents a Markov model for RAN slicing capable of characterizing diverse Radio Resource Management (RRM) strategies in multi-tenant and multi-service 5G scenarios including both guaranteed and non-guaranteed bit rate services. The proposed model captures the fact that different radio links from diverse users can experience distinct spectral efficiencies, which enables an accurate modeling of the randomness associated with the actual resource requirements. The model is evaluated in a multi-tenant scenario in urban micro cell and rural macro cell environments to illustrate the impact of the considered RRM polices in the QoS provisioning. INDEX TERMS Markov processes, radio access networks, RAN slicing, radio resource management, quality of service.
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