Mobile core network virtualization: A model for combined virtual core network function placement and topology optimization

Abstract

Ahstract-This paper addresses an important aspect of mobile core network virtualization: The combined optimization of the virtual mobile core network topology (graph) and its embedding onto a physical substrate network. Basically this comprises the placement of mobile core virtual network functions (VNFs) onto the nodes of the physical substrate network, the determination of the interconnections towards the radio access network (RAN) and the Internet as well as the traffic routing between the VNFs. This problem differs from the traditional virtual network embedding (VNE) problem as the virtual network topology is not known in advance and several additional constraints apply, e.g. not every node of the physical substrate network might be able to host every VNF. We assume that the topology, link capacities and node resources of the physical substrate network are predefined and that a node comprises both packet forwarding and datacenter/server functionality. The node capabilities are defined by the processing, storage and switching (throughput) resources as well as the ability to host specific mobile core VNFs, i.e. the SGW, PGW, MME and HSS virtual functions. For the traffic routing, explicit single path routing is assumed. We propose a novel integer linear programming formulation which combines the optimization of the virtual network topology with VNE optimization. Optimization target is to minimize the cost of occupied link and node resources. Our formulation relies on the joint embedding of individual core network service chains where a core network service chain denotes the sequence of mobile core VNFs a user or control plane traffic flow traverses. We evaluate our model by means of two physical network topology examples taken from SNDlib [1]. It is shown that our approach outperforms traditional VNE optimization approaches in terms of optimality and computation time.

Semantically Enhanced Mapping Algorithm for Affinity-Constrained Service Function Chain Requests

“…In previous work, we formulated the online virtual function mapping and scheduling problem and proposed a set of algorithms for solving it [29]. Other research focuses on specific NFV uses cases such as virtualizing the CPE, Evolved Packet Core (EPC) or Deep Packet Inspection (DPI) [30], [31], [32], [33]. For an extended overview of resource allocation in NFV, we refer to a survey by Herrera et al [34].…”

“…To balance the bandwidth consumption over the full infrastructure, the difference between the maximum and minimum load over all links is minimized as shown in Equation (32). To model this, two additional continuous decision variables Load min ∈ [0.0, 1.0] and Load max ∈ [0.0, 1.0] are added, as well as constraints stating that the load of each link should be smaller, respectively larger, than the maximum and minimum load.…”

Infrastructure planning and topology optimization for reliable mobile big data transmission under cloud radio access networks

“…Other related works have studied on topology and placement optimization, such as [16] and [17]. A model for combined virtual core network function placement and topology optimization is proposed in [16].…”

“…A model for combined virtual core network function placement and topology optimization is proposed in [16]. In [17], the author presents some optimization problems in WMNs and different heuristic methods such as local search, genetic algorithms, and tabu search for solving them nearoptimally.…”

Model for Efficient Allocation of Network Functions in Hybrid Environment

“…In the last years, many scientific contributions [22] addressed the virtual network embedding problem, either applying mathematical optimization models (e.g. [2], [11], [23]) or algorithmic approaches (e.g.…”