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.
Network slice resource allocation is a complex problem. When a slice instance is seen as a collection of SFCs, slice embedding needs to deploy the SFCs on a shared infrastructure while satisfying various constraints. Most of prior works related to SFC and VNF deployment do not account for coverage constraints. For example, in @cite_14 @cite_0 , computing, storage, and aggregated wireless resource demands of SFCs are considered. The minimization of the SFC embedding cost is formulated either as an (ILP) @cite_0 @cite_2 @cite_3 or as a (MILP) problem @cite_17 @cite_28 , which are known to be NP-hard @cite_27 . In @cite_24 , the VNF placement problem is expressed as an (IQP) problem with a set of energy consumption constraints, and then is transformed to a solvable linear form.
@cite_14 @cite_28 @cite_3 @cite_0 @cite_24 @cite_27 @cite_2 @cite_17
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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. Network slicing has recently appeared as a key enabler for the future 5G networks where Mobile Network Operators (MNO) 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 highly important for future deployment. In this paper, taking the InP perspective, we propose an optimization framework for slice resource provisioning addressing multiple slice demands in terms of computing, storage, and wireless capacity. We assume that the aggregated resource requirements of the various Service Function Chains to be deployed within a slice may be represented by a graph of slice resource demands. Infrastructure nodes and links have then to be provisioned so as to satisfy these resource demands. A Mixed Integer Linear Programming formulation is considered to address this problem. A realistic use case of slices deployment over a mobile access network is then considered. Simulation results demonstrate the effectiveness of the proposed framework for network slice provisioning. Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14 optimality-gap) while their execution time makes them usable for real-life networks. The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommodate different business models. In particular, infrastructure providers need to implement radically new admission control policies to decide on network slices requests depending on their Service Level Agreements (SLA). When implementing such admission control policies, infrastructure providers may apply forecasting techniques in order to adjust the allocated slice resources so as to optimize the network utilization while meeting network slices' SLAs. This paper focuses on the design of three key network slicing building blocks responsible for (i) traffic analysis and prediction per network slice, (ii) admission control decisions for network slice requests, and (iii) adaptive correction of the forecasted load based on measured deviations. Our results show very substantial potential gains in terms of system utilization as well as a trade-off between conservative forecasting configurations versus more aggressive ones (higher gains, SLA risk).