Deployment Of Virtual Network Functions Research Articles

Multilayer protection-at-lightpath for reliable slicing with isolation in optical metro-aggregation networks

The high reliability required by many future-generation network services can be enforced by proper resource assignments by means of logical partitions, i.e., network slices, applied in optical metro-aggregation networks. Different strategies can be applied to deploy the virtual network functions (VNFs) composing the slices over physical nodes, while providing different levels of resource isolation (among slices) and protection against failures, based on several available techniques. Considering that, in optical metro-aggregation networks, protection can be ensured at different layers, and the slice protection with traffic grooming calls for evolved multilayer protection approaches. In this paper, we investigate the problem of reliable slicing with protection at the lightpath layer for different levels of slice isolation and different VNF deployment strategies. We model the problem through an integer linear program (ILP), and we devise a heuristic for joint optimization of VNF placement and ligthpath selection. The heuristic maps nodes and links over the physical network in a coordinated manner and provides an effective placement of radio access network functions and the routing and wavelength assignment for the optical layer. The effectiveness of the proposed heuristic is validated by comparison with the optimal solution provided by the ILP. Our illustrative numerical results compare the impact of different levels of isolation, showing that higher levels of network and VNF isolation are characterized by higher costs in terms of optical and computation resources.

On the Upgrade of Service Function Chains With Heterogeneous NFV Platforms

The fast development of high-performance and flexible SmartNICs and programmable data plane switches (PDP-SWs) has motivated people to consider the deployment of virtual network functions (vNFs) on them. Hence, together with traditional virtual machines (VMs), SmartNICs and PDP-SWs form heterogeneous network function virtualization (NFV) platforms for realizing vNF service chains (vNF-SCs). In this work, we consider the transition from software-based homogeneous NFV platforms to the heterogeneous ones, and study how to optimize the service upgrade of vNF-SCs. Specifically, the service upgrade is divided into two steps, which are 1) selecting servers/switches in the substrate network (SNT) to upgrade, which is done by adding SmartNICs to servers and replacing traditional switches with PDP-SWs, under a fixed budget, and 2) redeploying the existing vNF-SCs in the updated SNT to maximize the quality-of-service (QoS) improvement on latency reductions. We first formulate an integer linear programming (ILP) model to optimize the overall service upgrade, then design two correlated optimizations for its two steps, and finally propose polynomial-time approximation algorithms to solve the optimizations. The results of extensive simulations confirm that our proposed algorithm outperforms the existing benchmarks in various network scenarios, and achieves better tradeoff between performance and time-efficiency.

Deep Reinforcement Learning-Based Algorithm for VNF-SC Deployment

Network function virtualization (NFV) has the potential to lead to significant reductions in capital expenditure and can improve the flexibility of the network. Virtual network function (VNF) deployment problem will be one of key problems that need to be addressed in NFV. To solve the problem of routing and VNF deployment, an optimization model, which minimizes the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF, is established. In this optimization model, the dependency among the different VNFs is considered. In order to solve the service chain mapping problem of high dynamic virtual network, a new virtual network function service chain mapping algorithm PDQN-VNFSC was proposed by combining prediction algorithm and DQN (Deep Q-Network). Firstly, the real-time mapping of virtual network service chains is modeled into a partial observable Markov decision process. Then, the real-time mapping process of virtual network service chain is optimized by using global and long-term benefits. Finally, the service chain of virtual network function is mapped through the learning decision framework of offline learning and online deployment. The simulation results show that, compared with the existing algorithms, the proposed algorithm has a lower the maximum index of used frequency slots, the number of used frequency slots, and the number of initialized VNF.

Online Adaptive Interference-Aware VNF Deployment and Migration for 5G Network Slice

Based on network function virtualization (NFV) and software defined network (SDN), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network slicing</i> is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the real-time network variations and diverse performance requirements among different 5G scenarios, online adaptive VNF deployment and migration are needed to dynamically accommodate to service-specific requirements. In this paper, we first propose a time-slot based 5G network slice model, which jointly includes both edge cloud servers and core cloud servers. Since VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantify the VNF interference. To achieve our objective—maximizing the total reward of accepted requests (i.e., the total throughput minus the weighted total VNF migration cost), we propose an Online Lazy-migration Adaptive Interference-aware Algorithm (OLAIA) for real-time VNF deployment and cost-efficient VNF migration in a 5G network slice, where an Adaptive Interference-aware Algorithm (AIA) is proposed as OLAIA’s core function for placing a given set of requests’ VNFs with maximized total throughput. Through trace-driven evaluations on two typical 5G network slices, we demonstrate that OLAIA can efficiently handle the real-time network variations and the VNF interference when deploying VNFs for real-time arriving requests. In particular, OLAIA improves the total reward by 22.18% in the autonomous driving scenario and by 51.10% in the 4K/8K HD video scenario, as compared with other state-of-the-art solutions.

NFV security survey in 5G networks: A three-dimensional threat taxonomy

Fifth Generation (5G) networks aim at providing value-added services with advanced performance such as low-latency communications, high reliability, high data rates and capacity to support an increasing number of connected devices. 5G networks are enabled by an automated and flexible provisioning and management of resources and services deployed over a shared infrastructure spanning multiple sites and domains and governed by different business players. This drives towards a complex 5G ecosystem enabled by Network Function Virtualization (NFV) and promoted by multiple business collaborations which draw new threats and vulnerabilities that are yet to be explored. Thus, we explore in this survey the complexity of the 5G ecosystem and derive a 5G telecommunication business model driven by 5G enabling technologies. We use this business model to identify new attack surface and security threats introduced by multiple business collaborations supported by NFV which we overview. Further, we delineate a three dimensional threat taxonomy for NFV-based 5G networks that leverages NFV architecture and deployment use cases in light of different 5G business collaboration scenarios. Finally, we shed light on insightful future research directions towards providing enhanced security in NFV-based 5G networks.

A measurement study of offloading virtual network functions to the edge

The deployment of virtual network functions (VNFs) at edge servers potentially impairs the performance of latency-sensitive applications due to their computational cost. This work considers a new approach to addressing this problem that provides line rate acceleration of VNFs by employing field-programmable gate array (FPGA) equipped edge servers. This approach has been validated by practical use cases with both TCP and UDP as underlying protocol on a physical testbed environment. We examine the performance implications of executing a security VNF at an FPGA-equipped edge server. We experimentally demonstrate reduced VNF execution latency and energy consumption for a real-time video streaming application in comparison with a software-only baseline. In particular, the results show that the approach lowers VNF execution latency and power consumption at the edge by up to 44% and 76%, respectively, in our experiments while satisfying time constraints and maintaining confidentiality with high scalability. We also highlight the potential research challenges to make this approach viable in practice.

A Semantic virtualized network functions description and discovery model

Network Function Virtualization (NFV) has increasingly gained importance to address some emerging networking challenges like agility and cost-effectiveness. NFV enables to run Virtualized Network Functions (VNF) on top of any generic, Commercial-Off-The-Shelf (COTS) hardware, anytime and anywhere in the network. Specific service providers offer VNFs to prospective network providers. Service providers publish VNFs in dedicated marketplaces where network providers search VNFs and instantiate them according to a pre-established service-level agreement. On top of being proprietary and specific to the service providers, the existing VNF description models include details on VNF deployment but fail to fit VNF functional and non-functional specifications. This description alters an efficient selection of the most relevant VNFs and prevents full automation of the VNFs provisioning. This paper introduces a novel domain-independent VIrtualized networK functIoN ontoloGy (VIKING for short) for VNF description and publication in federated repositories. It also proposes a semantic-based matchmaking algorithm to discover and select the most relevant VNFs that satisfy prospective VNF consumers’ requests. As for validation, a prototype called Mastermyr Chest, including VIKING’s instantiation along with the matchmaker in Content Delivery Networks (CDN) domain was implemented. This prototype illustrates a new way to contribute to the redesign of the CDN’s traditional architecture by enabling value-added CDN service provisioning in an agile and dynamic manner. A set of experiments was run to (i) evaluate the matchmaker performances and (ii) demonstrate its accuracy and precision.

Graph neural network‐based virtual network function deployment optimization

SummarySoftware‐defined networking (SDN) and network function virtualization (NFV) help reduce the operating expenditure (OPEX) and capital expenditure (CAPEX) as well as increase the network flexibility and agility. However, since the network is more dynamic and heterogeneous than before, operators have problems to cope with the increased complexity of managing virtual networks and machines. This complexity is paired with strict time requirements for making management decisions; traditional mechanisms that rely on, for example, integer linear programming (ILP) models are no longer feasible. Machine learning has emerged as one of the possible solution to address network management problems to get near‐optimal solutions in a short time. However, applying machine learning to network management is also not simple and has many challenges. Especially, understanding the network environment is an important problem for designing a machine learning model. In this paper, we proposed to use graph neural network (GNN) for virtual network function (VNF) management. The proposed model solves the complex VNF management problem in a short time and gets near‐optimal solutions. We developed a model by taking into account various network environment conditions so that it can be applied in the actual network environment. Also, through in‐depth experiments, we suggested the direction of the machine learning‐based network management method.

Customizable Deployment of NFV Services

Network Functions Virtualization (NFV) promotes a paradigm shift in the core network, by enabling the execution of network functions on a virtualized software plane instead of on dedicated hardware. Despite its benefits, NFV introduces new challenges, of which we highlight those related to the deployment of virtualized network services. Current NFV deployment solutions (i.e., those for composition, embedding, and scheduling) are usually limited to optimize hard-coded criteria, and cannot be customized to address specific demands defined by both network operators and NFV-as-a-Service customers. In this paper, we present a customizable NFV deployment framework that allows multiple criteria and multiple objectives to be applied to service composition, embedding, and scheduling. We evaluate the proposed framework integrated to deployment solutions specified in the literature. A case study is presented for the customized deployment of a traffic control and security service, and demonstrates the flexibility and effectiveness of the proposed framework.

Research on Optimized Deployment of Virtual Network Functions in Network Function Virtualization Environment

This paper mainly studies the optimization of dynamically arrived SFCs deployment in the SDN scenarios, and optimizes the end-to-end delay and bandwidth consumption during the deployment. First we build the model of the optimization problem, it is expressed as a 0-1 planning problem. We use ILP to get the optimal solution, cause the problem is NP hard, its runtime increases as network scales increases, so we choose the heuristic algorithm instead to reduce algorithm runtime. During our heuristic algorithm, we design a new method to sort VNFs and network nodes, then use the greedy algorithm to select nodes literately to place VNFs, in order to avoid local optimality, further use the simulated annealing algorithm with the results of the greedy algorithm as its initial solution, and design two methods to generate new deployments, and still repeat iterating to find a better deployment until reaching iteration limits. This paper also considers the SFCs’ lifecycle and trade-off between the two parameters. The simulation proves that the algorithm proposed in this paper can significantly reduce the end-to-end delay and bandwidth consumption than the traditional method, and 80% of its results are very close to the optimal solution with less than 5% error, and the ratio of its runtime and optimal solution’s is at most 0.003, the algorithm also has certain applicability and can be used in other scenarios.

Reliability-Aware Service Function Chain Backup Protection Method

Network Function Virtualization (NFV) decouples network functions from hardware, improves the flexibility of resource allocation, and enhances network scalability. Any failures of software and hardware in an NFV environment will result in the interruption of Service Function Chains (SFCs). As one of the key technologies of 5G, NFV has more stringent delay and reliability requirements for services. This paper takes software and hardware failures into account, and proposes a reliability-aware service function chain backup protection (RABP) method to meet SFCs' high reliability and low latency demands. First, we formulate a mixed-integer linear programming model to maximize the revenue-to-cost ratio. Then, we propose a heuristic algorithm for suboptimal solutions. SFC deployment is divided into two stages: primary virtual network function (VNF) deployment and backup VNF deployment. The reliability enhancement and delay optimization deployment method is presented to deploy primary VNFs. A breadth-first search method is used to consolidate primary VNFs as much as possible to improve reliability of SFCs and reduce transmission delay. When deploying backup VNFs, a resource-efficient backup selection method is presented to reduce backup resource consumption while meeting reliability demands. Finally, experiment results show that the proposed RABP method not only has the best acceptance ratio and long-term average revenue to cost ratio, but also reduces backup resource consumption and transmission delay.

Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

Due to the massive Internet of Things (IoT) connectivity and substantial growth of communication traffic, Virtual Network Function (VNF) orchestration scheme is anticipated to function promptly, dynamically, and intelligently for next-generation networks. Hence, we urge the necessity to move beyond the traditional paradigm and employ VNFs on the network edge located cloudlet. Overall, multi-access edge computing can intensify the performance of delay-sensitive IoT applications compared to the core cloud based VNF deployments. In this paper, we intend to investigate how to simultaneously leverage the ensembling of multiple deep learning models for proper calibration to provide real-time VNF placement solutions. We also address the challenges associated with state-of-the-art approaches to deal with dynamic network traffic and topology patterns. Our envisioned methods, based on Convolutional Neural Networks and Artificial Neural Networks named as E-ConvNets and E-ANN respectively, suggest two proactive VNF deployment strategies. These ensembled VNF deployment strategies demonstrate encouraging performance (optimality gap nearly 7%) in terms of minimizing relocation and communication costs, and high scalability intelligence factor (around 0.93) through simulation results compared to standalone deep learning models. Furthermore, the presented results indicate the potentialities of applying deep learning-based strategies into similar research enigmas for future telecommunication network researches.

Latency-Efficient VNF Deployment and Path Routing for Reliable Service Chain

Network services usually chain multiple types of Virtual Network Functions (VNFs) together in a specific order, known as service chain. One important issue of providing network services is reliability, which means that each type of VNF in a service chain acts properly on its function. The effective way to guarantee the reliability is to utilize sufficient redundancy through providing VNF backup instances beyond active primary ones. Software Defined Networking (SDN) enables the dedicate location pickup of primary and backup instances on switch-connected servers. The deployment of primary and backup VNF instances plays an important role of flow routing because of its influence on the transmission latency of the flow. In order to minimize the total latency of flows, we first formulate the reliable service chain deployment as a mathematical optimization problem. A detailed analysis of both software and hardware failure models is studied. Then we propose optimal solutions for the case of a single flow. Next, we prove the NP-hardness of our problem for the case with multiple flows and propose a heuristic strategy. Additionally, a performance-guaranteed solution is included if the server capacity is infinite. Extensive simulations are conducted to evaluate our proposed solutions compared to multiple algorithms.

Multi-Resource VNF Deployment in a Heterogeneous Cloud

The emerging paradigm of Network Function Virtualization (NFV) promises to shorten the renewal cycles of network functions and reduce the capital expenses by flexibly deploying virtualized network functions (VNFs) implementation on commodity servers. However, the required resource of each type (CPU, memory, etc.) for the running VNF should be provisioned to guarantee the performance when processing packets. This comes with different deployment cost, especially in a heterogeneous cloud consisting of a large number of network function platforms from various vendors. To optimally operate VNFs, it is necessary for the network operator to dynamically deploy VNFs in the expensive cloud infrastructures. In this article, we initiate the study of minimizing the deployment cost under multi-resource constraints in a heterogeneous cloud. We formulate multi-resource VNF deployment problem (MVDP) as an optimization program and prove its hardness. We propose an offline <inline-formula><tex-math notation="LaTeX">$(1, d+1)$</tex-math></inline-formula> -bicriteria approximation algorithm and an <inline-formula><tex-math notation="LaTeX">$(\mathcal {O}(1), \mathcal {O}(n \cdot \log n))$</tex-math></inline-formula> -competitive online algorithm to deploy VNFs in a scalable manner, where <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the number of resource types and <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of required VNFs. Large-scale simulations and DPDK-based OpenNetVM implementation show that our algorithms can reduce the overall cost by 34% and improve the performance in terms of multi-resource allocation.

IoT Ecosystem on Exploiting Dynamic VNF Orchestration and Service Chaining: AI to the Rescue?

An efficient automated virtual network function (VNF) deployment and service function chaining (SFC) can induce a significant improvement in the overall performance of various IoT services. Few concerns regarding the latency benefits, energy consumption expenditure, and migration costs are required to be taken into consideration collaboratively for the solution method to accommodate supreme privileges for both users and providers. However, most of the works existing in the literature emphasize these issues exclusively. Additionally, they focus on employing traditional mathematical programming-based approaches to find optimal solutions that are computationally expensive. Thus, state-of-the-art methods are infeasible and not prompt enough to provide real-time solutions for massive IoT services. In this article, we propose the utilization of different deep learning and reinforcement learning techniques (e.g., artificial neural networks, convolutional neural networks, deep Q-networks, and federated learning) for swift VNF orchestration and SFC. Moreover, we identify some challenges and their potential solutions associated with these sophisticated learning models. Then we present some simulation results on a VNF deployment case study demonstrating that deep learning techniques can be a significant breakthrough with promising potential to resolve most of the mentioned concerns incorporated with the VNF orchestration and SFC generation problem.

Optimal Virtual Network Function Deployment for 5G Network Slicing in a Hybrid Cloud Infrastructure

Network virtualization is a key enabler for 5G systems to support the expected use cases of vertical markets. In this context, we study the joint optimal deployment of Virtual Network Functions (VNFs) and allocation of computational resources in a hybrid cloud infrastructure by taking the requirements of the 5G services and the characteristics of the cloud architecture into consideration. The resulting mixed-integer problem is reformulated as an integer linear problem, which can be solved by using a standard solver. Our results underline the advantages of a hybrid infrastructure over a standard cloud radio access network consisting only of a central cloud, and show that the proposed mechanism to deploy VNF chains leads to high resource utilization efficiency and large gains in terms of the number of supported VNF chains. To deal with the computational complexity of optimizing a large number of clouds and VNF chains, we propose a simple low-complexity heuristic that attempts to find a feasible VNF deployment solution with a limited number of functional splits. Numerical results indicate that the performance of the proposed heuristic is close to the optimal one when the edge clouds are well dimensioned with respect to the computational requirements of the 5G services.

Deploying Virtual Network Functions With Non-Uniform Models in Tree-Structured Networks

Network Function Virtualization (NFV) has promoted the implementation of network functions from expensive hardwares to software middleboxes. These software middleboxes, also called Virtual Network Functions (VNFs), are executed on switch-connected servers. Efficiently deploying such VNFs on servers is challenging because the traffic rate of flows must be fully processed by their requested VNFs when they reach destinations, and the deployed positions of VNFs are restricted by the server capacity. In addition, each network function offers non-uniform VNF models (types) with different configurations of processing volumes and costs. This paper focuses on minimizing the total cost of deploying VNFs for providing a specific network function to all flows in tree-structured networks. First, we prove the NP-hardness of non-uniform VNF deployment in a tree topology and propose a dynamic programming based solution with a pseudo-polynomial time complexity. Then we narrow it down to three simplified cases by focusing on either uniform VNFs or the linear line topology. Specifically, three algorithms are introduced: an improved dynamic programming based algorithm for deploying uniform VNFs in a tree topology, a performance-guaranteed algorithm for deploying non-uniform VNFs in a linear line topology, and an optimal greedy algorithm for deploying uniform VNFs in a linear line topology. Additionally, we generalize our approach to a case of deploying a service chain, which consists of multiple network functions applied to flows in a specific order. We propose two solutions: one is optimal but time-consuming while another is heuristic but efficient. Extensive simulations are conducted to evaluate our algorithms.

Sum of squares: a new metric for NFV service chain placement in edge computing environments and efficient heuristic algorithms

Both network function virtualization (NFV) and edge computing (EC), especially the latter, are attracting more and more attention in recent years. A growing number of network service providers are migrating their services from the cloud to the edge for better QoS services, while the recent researches on NFV also concentrate on deploying NFV services in edge computing networks. However, NFV deployment in edge networks is a troublesome challenge and is fairly alien from conventional NFV deployment problems in data centres. Edge network differs from the data center network in the following two aspects: firstly, edge nodes are constrained in computing capacity, and secondly, the network connections between edge nodes are unstable and dynamic, which may show large variance over time. This means edge computing should be designed for high-efficient use of physical edge nodes’ resources. To address the challenges above, we investigate a new NFV Service Chain Placement problem in edge computing environments (NSCP-EC) in this paper. We first prove that the NSCP-EC problem is NP-complete. Then we propose a new metric which can better measure the capacity utilization rate of physical resources, and analyze its advantages with details. Based on the new metric, we propose two heuristic but efficient algorithms called MINI and MINI-tree. To confirm the performance of the two algorithms, we conduct simulations. The result demonstrates that MINI gains an advantage over genetic algorithm (GA) and MINI-tree orevails over MINI in tree topology conditions in the aspects of physical resource utilization, acceptance rate and running time. Both theoretical analysis and simulation results confirm the feasibility of the algorithms.

A Reliability-aware 5G Network Slice Reconfiguration and Embedding Algorithm

Considering the problems of low resource utilization and poor reliability of traditional network slice embedding, a Reliability-aware Network Slice (NS) Reconfiguration and Embedding (RNSRE) strategy is proposed. Firstly, a utility function of reliable embedding oriented reliability and available resources is established. Then, considering the resource requirements and the location constraints of Virtual Network Function (VNF), a method is proposed to quantify the reliability requirement of VNF. Based on the above works, the reliable network slice embedding problem is formulated as an integer linear programming which maximizes the profits of reliable VNF deployment while minimizing the consumption of link bandwidth resource. Finally, according to different types of network slices, a network slice reliable embedding algorithm based on neighborhood search and a network slice reconfiguration embedding algorithm based on key VNF backup are proposed. Simulation results show that the proposed algorithms improve the resources utilization and reduce the embedding cost while meeting the reliability of VNF.

An Online Algorithm for VNF Service Chain Scaling in Datacenters

Built on top of virtualization technologies, network function virtualization (NFV) provides flexible and scalable software implementation of various network functions. Virtual network functions (VNFs), which are network functions implemented as virtual machines, are chained together to provide network services. Dynamic deployment of VNFs while satisfying incoming network traffic demand is the key to cost optimization of an NFV system. Besides considering server resource capacity and incoming traffic rates, an optimal scaling policy needs to strike a balance between VNF’s operational costs, the costs for maintaining VNF instances, and VNF deployment costs, additional costs when setting up new VNF instances on a server. This paper targets dynamic scaling of VNF instances in a cloud data center where multiple VNF chains are running. We propose an online scaling algorithm to adjust the deployment of VNF instances according to time-varying traffic demand, ensuring a good competitive ratio. Through theoretical analysis and trace-driven simulation, we demonstrate effectiveness of the proposed online VNF scaling algorithm.