Maximizing Infrastructure Providers' Revenue Through Network Slicing in 5G

Adapting to recent trends in mobile communications towards 5G, infrastructure owners are gradually modifying their systems for supporting the network programmability paradigm and for participating in the slice market (i.e., dynamic leasing of virtual network slices to service providers). Two-fold are the advantages offered by this upgrade: i) enabling next generation services, and ii) allowing new profit opportunities. Many efforts exist already in the field of admission control, resource allocation and pricing for virtualized networks. Most of the 5G-related research efforts focus in technological enhancements for making existing solutions compliant to the strict requirements of next generation networks. On the other hand, the profit opportunities associated to the slice market also need to be reconsidered in order to assess the feasibility of this new business model. Nonetheless, when economic aspects are studied in the literature, technical constraints are generally oversimplified. For this reason, in this work, we propose an admission control mechanism for network slicing that respects 5G timeliness while maximizing network infrastructure providers' revenue, reducing expenditures and providing a fair slice provision to competing service providers. To this aim, we design an admission policy of reduced complexity based on bid selection, we study the optimal strategy in different circumstances (i.e., pool size of available resources, service providers' strategy and trafic load), analyze the performance metrics and compare the proposal against reference approaches. Finally, we explore the case where infrastructure providers lease network slices either on-demand or on a periodic time basis and provide a performance comparison between the two approaches. Our analysis shows that the proposed approach outperforms existing solutions, especially in the case of infrastructures with large pool of resources and under intense trafic conditions.Peer ReviewedPostprint (published version

Optimal Cross Slice Orchestration for 5G Mobile Services

5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed framework and solution are efficient not only in providing slice-as-a-service based on the service requirements, but also in maximizing the provider's revenue.Comment: 6 pages, 6 figures, WCNC 2018 conferenc

Slice Admission control based on Reinforcement Learning for 5G Networks

Network slicing empowers service providers to deploy diverse network slice architectures within a shared physical infrastructure. This technology enables the provision of differentiated services that cater for specific Quality of Service (QoS) requirements of different use cases which need to be adequately supported in 5G networks. By leveraging Network Slicing, operators can effectively meet these diverse requirements and provide customized services to different tenants in a flexible and efficient manner. However, infrastructure providers face a challenging dilemma of the slice admission control regarding whether to accept or reject slice requests. From one perspective, they strive to optimize the utilization of network resources through accepting a significant number of network slices. From another perspective, the availability of network resources is restricted, and it is crucial to fulfil the QoS requirements specified by the network slices. In this research, an Admission Control (AC) Algorithm founded upon Reinforcement Learning mechanisms, specifically Q-Learning (QL), Double Q-Learning (Double-QL), and a proposed mechanism based on Double QL is obtained to overcome this challenge. This algorithm is applied in order to make informed decisions regarding network slice requests. The simulation results demonstrate that the AC algorithm, leveraging the suggested mechanism, surpasses the Double-QL and QL mechanisms in relation to gained profit with average of 8% and 26%, respectively. In case of the acceptance ratio of slice requests, it achieves average of 13% and 28% higher than Double-QL and QL mechanisms, respectively. Finally, it obtains the maximum resource utilization, surpassing Double-QL and QL by 9% and 20%, respectively

Slice Admission control based on Reinforcement Learning for 5G Networks

Network slicing empowers service providers to deploy diverse network slice architectures within a shared physical infrastructure. This technology enables the provision of differentiated services that cater for specific Quality of Service (QoS) requirements of different use cases which need to be adequately supported in 5G networks. By leveraging Network Slicing, operators can effectively meet these diverse requirements and provide customized services to different tenants in a flexible and efficient manner. However, infrastructure providers face a challenging dilemma of the slice admission control regarding whether to accept or reject slice requests. From one perspective, they strive to optimize the utilization of network resources through accepting a significant number of network slices. From another perspective, the availability of network resources is restricted, and it is crucial to fulfil the QoS requirements specified by the network slices. In this research, an Admission Control (AC) Algorithm founded upon Reinforcement Learning mechanisms, specifically Q-Learning (QL), Double Q-Learning (Double-QL), and a proposed mechanism based on Double QL is obtained to overcome this challenge. This algorithm is applied in order to make informed decisions regarding network slice requests. The simulation results demonstrate that the AC algorithm, leveraging the suggested mechanism, surpasses the Double-QL and QL mechanisms in relation to gained profit with average of 8% and 26%, respectively. In case of the acceptance ratio of slice requests, it achieves average of 13% and 28% higher than Double-QL and QL mechanisms, respectively. Finally, it obtains the maximum resource utilization, surpassing Double-QL and QL by 9% and 20%, respectively

A machine learning approach to 5G infrastructure market optimization

It is now commonly agreed that future 5G Networks will build upon the network slicing concept. The ability to provide virtual, logically independent "slices" of the network will also have an impact on the models that will sustain the business ecosystem. Network slicing will open the door to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this paper, we address this issue by designing a network slice admission control algorithm that (i) autonomously learns the best acceptance policy while (ii) it ensures that the service guarantees provided to tenants are always satisfied. The contributions of this paper include: (i) an analytical model for the admissibility region of a network slicing-capable 5G Network, (ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure providers revenue, and (iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R). The work of NEC Laboratories Europe was supported by the 5G-Transformer project (Grant Agreement No. 761536)

Network slicing to enable scalability and flexibility in 5G mobile networks

We argue for network slicing as an efficient solution that addresses the diverse requirements of 5G mobile networks, thus provid-ing the necessary flexibility and scalability associated with future network implementations. We elaborate on the challenges that emerge when we design 5G networks based on network slicing. We focus on the architectural aspects associated with the coexistence of dedicated as well as shared slices in the network. In particular, we analyze the realization options of a flexible radio access network with focus on network slicing and their impact on the design of 5G mobile networks. In addition to the technical study, this paper provides an investigation of the revenue potential of network slicing, where the applications that originate from such concept and the profit capabilities from the network operator's perspective are put forward.This work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA