Expanding the market of mobile network services and defining solutions that are cost efficient are the key challenges for next generation mobile networks. Network slicing is commonly considered to be the main instrument to exploit the flexibility of the new radio interface and core network functions. It targets splitting resources among services with different requirements and tailoring system parameters according to their needs. Regulation authorities also recognize network slicing as a way of opening the market to new players who can specialize in providing new mobile services acting as “tenants” of the slices.
Resources can also be distributed between infrastructure providers and tenants so that they meet the requirements of the services offered. In this paper, we propose a model for dynamic trading of mobile network resources in a market that enables automatic optimization of technical parameters and of economic prices according to high level policies defined by the tenants. We introduce a mathematical formulation for the problems of resource allocation and price definition and show how the proposed approach can cope with quite diverse service scenarios presenting a large set of numerical results.
Key words-Network slicing, infrastructure sharing, wireless market, pricing mechanism, dynamic resource sharing
The first well known factor that is challenging this model is the exponential growth of mobile traffic (cf. ) that is pushing operators to rapidly expand the capacity of their network with technology upgrades, coverage densification, and spectrum refarming. Unfortunately, the average revenues per user are not growing with the same pace (in some countries they are even decreasing) and the number of traditional users can no longer be increased.
This is leading to an aggressive cost optimization and reduction that is not sustainable in the long run. A possible solution to the problem is the evolution of the technology towards supporting a larger set of applications beside the traditional mobile broadband. It is important, that not only the market expands but we use the network infrastructure intelligently as well to further stimulate the digital growth. Research and standardization work items on 5G networks during the past few years have similarly been focusing on forming a new technology not only to be able to improve the performance of the previous network technologies, but also to support a wide range of vertical applications with diverse and stringent requirements in terms of throughput, delay, reliability and energy . However, due to some fundamental technical limits, increasing the performance significantly, while satisfying all these heterogeneous constraints, is simply not possible, and the network must be optimized depending on the specific application domain. The concept of network slicing has been introduced with the goal of allowing resource allocation to different applications and traffic classes so that it meets the various quality requirements .
Even if network slicing can be seen as a precious tool for operators to manage their new generation networks, it poses new challenges as well. A straightforward way of allocating resources to different slices is through (almost) static partitioning, which can however lead to low efficiency. Dynamic resource allocation can be a solution, but it must accurately consider traffic evolution and performance constraints of all applications. Slicing the network might naturally generate new participants in the market. The operators of the network slices, named “tenants” in the 5G terminology, acquire resources from the traditional operators, who are turning into infrastructure providers in this changing environment. From the regulation authorities’ perspective, using slicing as a tool for infrastructure sharing is a way of creating new market opportunities and exploring new spectrum licensing strategies.
The idea of infrastructure sharing among multiple virtual mobile operators has long been under considerations. Among the alternative sharing approaches listed by the Organization for Economic Co-operation and Development (OECD) report, active sharing is considered to be the most cost-efficient sharing approach . Active sharing includes sharing both active network elements and spectrum resources. Virtual operators can then share resources with other operators and decrease costs . Although a number of different sharing scenarios exist, the most common one includes a single infrastructure provider and a set of virtual mobile network operators (MVNOs) who acquire resources to serve their users. Note that MVNOs and tenants are similar in the sense that they both manage resources and can provide specialized services, the former in legacy networks while the latter as independent entities. For a given quality target, sharing allows saving resources by exploiting the multiplexing gain. The increased efficiency in resource usage and the adaptability to traffic conditions, are clear advantages . Infrastructure sharing has some similarities with resource sharing in Cognitive Radio Networks (CRNs), but with the fundamental difference that tenants (or MVNOs) have equal rights to access resources and, therefore, the problem is basically about resource negotiation rather than opportunistic access.
In this paper, we propose a dynamic wireless market model that can flexibly adjust the share of resources, assigned to network slices, to achieve the maximum utility for tenants. The contributions of this work can be summarized as follows.
· An enhanced wireless market model based on different services and quality requirements using dynamic pricing through the formulation (1a)-(1h) in Section III-A
· A two-step approach for adapting the network slices according to the fluctuations of the achievable rate and the variations of the traffic mix in short time scale in Section III-B
· A dynamic updating mechanism for optimizing the slice configuration based on the evolution of the resource distributions over time and the achieved spectral efficiency in Section III-C
· Exploitation of the anticipatory information of the achievable rates for the resource allocation in Section III-D The remainder of the paper is organized as follows: Section II contains the system model and the main assumptions.
Following the system model, the optimization model is presented in Section III. In Section IV, the behavior and the validity of the optimization model are investigated through simulations. Section V concludes the paper and discusses possible extensions of the proposed approach.
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