Effective network slicing requires an infrastructure network provider to deal with the uncertain demand and real-time dynamics of network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This article develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demand from tenants. Specifically, we first propose a novel system model which enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state action spaces. This makes Q-learning practically inapplicable to our case in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with real-time resource requests and the dynamic demands of users. Extensive simulations show that the proposed framework yields up to 40 higher long-term average return while being few thousand times faster, compared with state of the art network slicing approaches.
A number of research works have been introduced recently to address the network slicing resource allocation problem for the network provider @cite_28 - @cite_0 . In particular, the authors in @cite_28 and @cite_2 developed a two-tier admission control and resource allocation model to answer two fundamental questions, i.e., whether a slice request is accepted and how much radio resource is allocated to the accepted slice. To address this problem, the authors in @cite_28 used an extensive searching method to achieve the globally optimal resource allocation solution for the network provider. However, this searching method cannot be applied to complex systems with a large number of resources. To address this problem, a heuristic scheme with three main steps was introduced in @cite_2 to effectively allocate resources to the users. Yet this heuristic scheme cannot guarantee to achieve the optimal solution for the network provider. In addition, both network slicing resource allocation solutions proposed in @cite_28 and @cite_2 are heuristic methods with only radio resource taken into consideration. Thus, these solutions may not be appropriate to implement in dynamic network slicing resource allocation systems with a wide range of resource demands and services.
@cite_28 @cite_0 @cite_2
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5G mobile network is expected to serve flexible requirements hence dynamically allocate network resources according to the demands. Network slicing, where network resources are packaged and assigned in an isolated manner to set of users according to their specific requirements, is considered as a key paradigm to fulfil diversity of requirements. There will clearly be conflicting demands in allocation of such slices, and the effective provisioning of network slicing poses several challenges. Indeed, network slicing has a twofold impact in terms of user traffic prioritization as it dictates for the simultaneous management of the priority among different slices (i.e., interslice) and the priority among the users belonging to the same slice (i.e., intra-slice). In this paper, we propose a novel heuristicbased admission control mechanism able to dynamically allocate network resources to different slices in order to maximize the satisfaction of the users while guaranteeing to meet the requirements of the slices they belong to. Through simulations, we demonstrate how our proposal provides (i) higher user experience in individual slices, (ii) increased utilization of network resources and (iii) higher scalability when the number of users in each slice increases. A method of providing radio resources to a number of communication flows in a radio access network that comprises a slice controller and a plurality of base stations. The method comprises in the slice controller abstracting radio resources that are available to the plurality of base stations and dynamically allocating the abstracted radio resources to a number of isolated slices, each slice accommodating one or more of the communication flows and communicating the radio resource allocations to the base stations. The method further comprises, in the base stations, performing application specific customisation radio resources within an allocated slice. Wireless virtualization is a promising approach to foster innovation and prevent the ossification of wireless networks. Within a virtualized wireless network, multiple network slices, or virtual operators (VO), are co- hosted on the same physical infrastructure. A fundamental question in this environment is which multiplexing technique, TDMA, FDMA or SDMA, should be used to slice the network among the VOs. Another related question is how should the stochastic arrival process affect the slicing and QoS criteria. To answer these two questions, we study the problem of QoS-aware joint admission control and network slicing. Due to the NP- hardness of the problem, we approach it using a heuristic algorithm composed of three steps: spectrum allocation, admission control and spatial multiplexing. The proposed algorithm incorporates the effects of QoS and stochastic traffic. We study through simulations the benefits of joint spatial- frequency multiplexing over the static frequency slicing approach. Finally, our simulation results help shed some light on the trade-offs between frequency and spatial multiplexing as well as between QoS and utilization.