LawNFO: A decision framework for optimal location-aware network function outsourcing

Gil

2020

Int J Communication

Summary In 5G networks, mobile network and service functions run as software on general‐purpose servers that are distributed across remote sites. Dedicated networks, such as Internet of Things (IoT) network, disaster network, and immersive media network, are virtualized as software solutions on common hardware equipment. This means a new network can be easily provisioned by modifying the configurations of the network based on software‐defined networking (SDN) and network functions virtualization (NFV). Furthermore, the 5G core network architecture toward flat architecture with distributed cloud is a key solution for providing service flexibility and scalability in order to handle an explosive increase in mobile data traffic. To create heterogeneous networks and services on 5G networks, we propose a location‐aware network virtualization method that aims at minimizing the network load and resource cost by appropriately selecting the relevant cloud (including Virtual Machines (VMs), virtual network function [VNF] nodes, applications, and resources) and mobile core entities to provide the required virtual networking services that facilitate secure isolation of the virtual networks with automated resource convergence. In addition, we propose a selective mobility management scheme for service continuity in cases of user equipment (UE) handover. The numerical results show the trade‐off between the different cost factors, ie, network cost and service resources cost in terms of the number of deployed clouds on 5G core networks.

Section: Background and Related Workmentioning

confidence: 99%

A location‐aware network virtualization and reconfiguration for 5G core network based on SDN and NFV

Gil

2020

Int J Communication

IEEE Trans. Netw. Serv. Manage.

“…The authors in [31] use machine learning techniques to find an optimal placement for VNFs given data center resources. An optimal locationaware VNF mapping is proposed in [32], that minimizes the function processing and traffic transmission cost. For mobile networks, an optimization for the network resources, i.e., link and node capacity, has been proposed in [33] for the embedding of virtual mobile core network functions.…”

Section: B Dimensioning and Resource Allocation Problemsmentioning

confidence: 99%

Towards a Cost Optimal Design for a 5G Mobile Core Network Based on SDN and NFV

Basta

Blenk

Hoffmann

et al. 2017

With the rapid growth of user traffic, service innovation, and the persistent necessity to reduce costs, today's mobile operators are faced with several challenges. In networking, two concepts have emerged aiming at cost reduction, increase of network scalability and deployment flexibility, namely Network Functions Virtualization (NFV) and Software Defined Networking (SDN). NFV mitigates the dependency on hardware, where mobile network functions are deployed as software Virtual Network Functions (VNF) on commodity servers at cloud infrastructure, i.e., data centers (DC). SDN provides a programmable and flexible network control by decoupling the mobile network functions into control plane and data plane functions. The design of the next generation mobile network (5G) requires new planning and dimensioning models to achieve a cost optimal design that supports a wide range of traffic demands. We propose three optimization models that aim at minimizing the network load cost as well as data center resources cost by finding the optimal placement of the data centers as well the SDN and NFV mobile network functions. The optimization solutions demonstrate the trade-offs between the different data center deployments, i.e., centralized or distributed, and the different cost factors, i.e., optimal network load cost or data center resources cost. We propose a Pareto optimal multi-objective model that achieves a balance between network and data center cost. Additionally, we use prior inference, based on the solutions of the single objectives, to pre-select data center locations for the multi-objective model that results in reducing the optimization complexity and achieves savings in run time while keeping a minimal optimality gap.

“…Fan et al [78] consider the problem of availability-aware SFC mapping. The authors propose a novel enhanced Joint Protection (JP) approach which is better than traditional Dedicated Protection (DP) and Shared Protection Introduces an analytic model and several metrics to evaluate the approaches Ghaznavi et al [49] MIP Heuristic Distributed VNF deployment and workload balancing Luizelli et al [50] ILP Heuristic Analysis the topological components of SFC requests Németh et al [65] N/A Heuristic Proposes a fine-tuning of real-time parameterizable orchestration algorithm Guerzoni et al [66] MIP N/A Presents a comprehensive analysis and model of reliability requirements Bari et al [18] ILP Heuristic Introduces network transformation to simplify the formulation Eramo and Miucci [67] ILP Heuristic Proposes a consolidation algorithm for consumption minimization Gupta et al [16] ILP N/A Presents a comprehensive mathematical model Cohen et al [17] ILP Approximation Provides approximation algorithm with theoretically proven performance Xia et al [19] BIP Heuristic Considers conversion cost in packet/optical datacenters Mehraghdam et al [13] MIQCP N/A Presents a context-free language to formalize the service requests Addis et al [14] MILP Heuristic Considers flow compression/decompression constraints Moens and De Turck [15] MILP N/A Considers the scenario of coexisting with physical functions Huberman and Sharma [68] CAP 2 Exact Leverages comparative advantage to place VNFs Lin et al [69] MIP, GT 3 Heuristic Presents an algorithm based on iterative weakly dominated elimination Bruschi et al [70] GT N/A An energy-aware Game-Theory-based solution for VNFs placement Martini et al [71] RCSP 4 N/A A layered structure that ensures the order of VNFs specified in the request Suksomboon et al [72] IP 5 Heuristic A framework to identify which VNF is worth to be outsourced to the cloud Yoshida et al [73] N/A Metaheuristic Considers multi-objective VNF placement Rost and Schmid [74] IP Approximation A polynomial algorithm for service chain embedding Gember et al [ Develops a heuristic to solve the problem incrementally Lukovszki and Schmid [11] ILP Deterministic Proposes a deterministic online algorith...…”

Section: A Resiliencymentioning

confidence: 99%

Service Function Chaining Resource Allocation: A Survey

Xie¹,

Liu²,

Wang³

et al. 2016

Preprint

Service Function Chaining (SFC) is a crucial technology for future Internet. It aims to overcome the limitation of current deployment models which is rigid and static. Application of this technology relies on algorithms that can optimally mapping SFC to substrate network. This category of algorithms is referred as "Service Function Chaining Resource Allocation (SFC-RA)" algorithms or "VNF placement (VNFP)" algorithms. This paper presents a survey of current researches in SFC-RA algorithms. After presenting the formulation and related problems, several variants of SFC-RA problem are summarized. At last, we discussed several future research directions.