Network slicing appears as a key enabler for the future 5G networks. Mobile Network Operators create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), sharing the resources across a set of network slices is important for future deployment. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account. Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the InP will be able to allocate enough resources to cope with the increasing demands of some SP. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints. The resource requirements of the various Service Function Chains to be deployed within a slice are aggregated within a graph of Slice Resource Demands (SRD). Coverage and rate constraints are also taken into account in the SRD. Infrastructure nodes and links have then to be provisioned so as to satisfy all types of resource demands. This problem leads to a Mixed Integer Linear Programming formulation. A two-step deployment approach is considered, with several variants, depending on whether the constraints of each slide to be deployed are taken into account sequentially or jointly. Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph representing the nodes and links on which resources have been provisioned. Simulation results demonstrate the effectiveness of the proposed approach compared to a more classical direct slice embedding approach.
Early results on assigning infrastructure network resources to virtual network components may be found, , in @cite_22 @cite_17 . Due to its capability of sharing efficiently network resource in 5G networks, the concept of network virtualization has gained renewed attention in the literature @cite_13 @cite_15 @cite_21 @cite_39 via the concept of network slicing.
@cite_22 @cite_21 @cite_39 @cite_15 @cite_13 @cite_17
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The concepts of network function virtualization and end-to-end network slicing are the two promising technologies empowering 5G networks for efficient and dynamic network service deployment and management. In this paper, we propose a resource allocation model for 5G virtualized networks in a heterogeneous cloud infrastructure. In our model, each network slice has a resource demand vector for each of its virtual network functions. We first consider a system of collaborative slices and formulate the resource allocation as a convex optimization problem, maximizing the overall system utility function. We further introduce a distributed solution for the resource allocation problem by forming a resource auction between the slices and the data centers. By using an example, we show how the selfish behavior of non-collaborative slices affects the fairness performance of the system. For a system with non-collaborative slices, we formulate a new resource allocation problem based on the notion of dominant resource fairness and propose a fully distributed scheme for solving the problem. Simulation results are provided to show the validity of the results, evaluate the convergence of the distributed solutions, show protection of collaborative slices against non-collaborative slices and compare the performance of the optimal schemes with the heuristic ones. Assigning the resources of a virtual network to the components of a physical network, called Virtual Network Mapping, plays a central role in network virtualization. Existing approaches use classical heuristics like simulated annealing or attempt a two stage solution by solving the node mapping in a first stage and doing the link mapping in a second stage. The contribution of this paper is a Virtual Network Mapping (VNM) algorithm based on subgraph isomorphism detection: it maps nodes and links during the same stage. Our experimental evaluations show that this method results in better mappings and is faster than the two stage approach, especially for large virtual networks with high resource consumption which are hard to map. In next generation wireless mobile networks, network virtualization will become an important key technology. In this paper, we firstly propose a resource allocation scheme for enabling efficient resource allocation in wireless network virtualization. Then, we formulate the resource allocation strategy as an optimization problem, considering not only the revenue earned by serving end users of virtual networks, but also the cost of leasing infrastructure from infrastructure providers. In addition, we develop an efficient alternating direction method of multipliers (ADMM)-based distributed virtual resource allocation algorithm in virtualized wireless networks. Simulation results are presented to show the effectiveness of the proposed scheme. 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.