The combination of recent emerging technologies such as network function virtualization (NFV) and network programmability (SDN) gave birth to the Network Slicing revolution. 5G networks consist of multi-tenant infrastructures capable of offering leased network "slices" to new customers (e.g., vertical industries) enabling a new telecom business model: Slice-as-a-Service (SlaaS). In this paper, we aim i) to study the slicing admission control problem by means of a multi-queuing system for heterogeneous tenant requests, ii) to derive its statistical behavior model, and iii) to provide a utility-based admission control optimization. Our results analyze the capability of the proposed SlaaS system to be approximately Markovian and evaluate its performance as compared to legacy solutions.

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The emerging feature of network slicing in future Fifth Generation (5G) networks calls for efficient slice management. Recent studies have been focusing on the mechanism of slice admission control, which functions in a manner of state machine. This paper proposes a general state model for synchronous slice admission control, and proves it to be Markovian under a set of weak constraints. An analytical approximation of the state transition matrix to reduce computational complexity in practical applications is also proposed and evaluated. Index Terms-5G, network slicing, network operations and management, network function virtualization I. INTRODUCTION N ETWORK slicing [1] has been considered as an essential feature and one of the most important enablers of the Fifth Generation (5G) cellular communications networks. It allows mobile network operators (MNOs) to manage and utilize their physical and virtual network resources, i.e. the network infrastructure and the capacity of virtualized network functions (VNFs), in the form of logically independent virtual mobile networks, a.k.a. network slices. It provides broad improvements of scalability, flexibility, accountability, shareability and profitability to cellular networks [2], [3].One emerging challenge brought by network slicing is to efficiently allocate network resources over different slices towards better utility efficiency. More specifically, there are two typical scenarios of such inter-slice resource management. First, when an MNO directly provides services to end users and maintains these services on its own scalable slices, as proposed in [4]. Second, in the resource-sharing use case of socalled Slice as a Service (SlaaS), which is discussed in [5], an MNO packs its resources into standardized atomic slices and rents them to external tenants such as virtual MNOs (VMNOs) and service providers for agreement-based periodical revenue. In both scenarios, the MNO aims to maximize the overall network utility rate (e.g. the revenue rate) by adjusting its resource allocation subject to the constraints of resource pool limit and regulation rules.Compared to the case of MNO's own service optimization, the SlaaS problem is more challenging due to the stochastic nature and fluctuating behavior of tenant demand for resources. Recently, multiple studies have been carried out on this topic [6]-[8], applying the similar framework where a binary decision is made by the MNO for every slice admission, i.e. to accept or decline the tenant request for a new slice. Most of these work consider the system as a state machine, where state transitions are triggered by the MNO's responses to randomly arriving tenant requests.

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