Deployment Of Virtual Network Functions Research Articles

Bidirectional Service Function Chain Embedding for Interactive Applications in Mobile Edge networks

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users' latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

A hybrid model for VNF deployment capable of responding to online requests at network edge

Network function virtualization (NFV) is a new network architecture that replaces dedicated hardware appliances with software instances and run them via software virtualization on general-purpose servers at edge clouds. As edge clouds are resource-constrained, optimal placement of virtual network functions (VNFs) is a challenging task. This problem has proven to be NP-hard, and thus, metaheuristic algorithms have been widely used to solve it. However, these techniques suffer from high computational complexity due to their iterative, time-consuming process and cannot be performed in real-time for online requests. This paper proposes a hybrid model based on heuristic algorithms and the Whale Optimization Algorithm (WOA) to optimally assign VNFs to physical servers at edge clouds. In this method, a multi-criteria heuristic model is used for online VNF deployment, while WOA is performed offline to tune the hyperparameters of the heuristic model. Simulation results demonstrate the efficiency of the proposed method against existing techniques in terms of power consumption and acceptance rate.

Collaborative Filtering-based Fast Delay-aware algorithm for joint VNF deployment and migration in edge networks

As Network Function Virtualization (NFV) continues to advance, Virtual Network Functions (VNFs) such as firewalls are increasingly used. Service Function Chains (SFCs) are formed by combining specific VNFs in a particular order, which are then deployed on the physical network to provide dedicated services to end users. Occasionally, partial VNF migration is employed to maintain service and network stability. However, decision-making times and system delays may become unacceptable due to the heterogeneous resource requirements of VNFs and the massive state migration of VNFs, especially in Multi-access Edge Computing (MEC) networks where resources are scarce and demand fluctuations are frequent. To solve these challenges, we first formulate the problem of Minimizing decision Time and system Latency for joint VNF Deployment and Migration (MTLDM) as a multi-objective optimization problem. Then, we propose a Collaborative Filtering-based Fast Delay-aware algorithm (CFFD) to solve this problem. In this algorithm, we introduce an innovative approach, referred to as the collaborative filtering-based method, which utilizes the preference information of deployed/migrated VNFs to assist the current VNF deployment/migration in reducing decision-making time. Additionally, we design a similarity-based method in CFFD to search for suitable hosts for the current VNF, thereby reducing the complexity caused by heterogeneity and minimizing system latency. Furthermore, we implement a heuristic method in CFFD to increase the number of accepted requests. In the end, extensive simulations are conducted to evaluate the performance of CFFD in comparison with baseline algorithms and to select the suitable similarity algorithm. The results of the selection simulation show that Manhattan distance and cosine similarity are superior to Pearson’s correlation. Moreover, the comparison simulation results indicate that CFFD outperforms the baseline algorithms in terms of delay optimization and decision time by up to 22.08% and 99%, respectively.

Online Joint Optimization of Virtual Network Function Deployment and Trajectory Planning for Virtualized Service Provision in Multiple-Unmanned-Aerial-Vehicle Mobile-Edge Networks

The multiple-unmanned-aerial-vehicle (multi-UAV) mobile edge network is a promising networking paradigm that uses multiple resource-limited and trajectory-planned unmanned aerial vehicles (UAVs) as edge servers, upon which on-demand virtual network functions (VNFs) are deployed to provide low-delay virtualized network services for the requests of ground users (GUs), who often move randomly and have difficulty accessing the Internet. However, VNF deployment and UAV trajectory planning are both typical NP-complete problems, and the two operations have a strong coupling effect: they affect each other. Achieving optimal virtualized service provision (i.e., maximizing the number of accepted GU requests under a given period T while minimizing the energy consumption and the cost of accepting the requests in all UAVs) is a challenging issue. In this paper, we propose an improved online deep reinforcement learning (DRL) scheme to tackle this issue. First, we formulate the joint optimization of the two operations as a nonconvex mixed-integer nonlinear programming problem, which can be viewed as a sequence of one-frame joint VNF deployment and UAV-trajectory-planning optimization subproblems. Second, we propose an online DRL based on jointly optimizing discrete (VNF deployment) and continuous (UAV trajectory planning) actions to solve each subproblem, whose key idea is establishing and achieving the coupled influence of discrete and continuous actions. Finally, we evaluate the proposed scheme through extensive simulations, and the results demonstrate its effectiveness.

On the Optimal Deployment of Virtual Network Functions in Non-Terrestrial Segments

In the upcoming Sixth-Generation (6G) of mobile communication systems, space network entities will cooperate with conventional terrestrial networks to provide three-dimensional wireless connectivity around the World. By considering the resulting massive amount of data to be managed into non-terrestrial segments, it will be necessary to dynamically configure functionalities across space network entities. Preliminary contributions in this context focus on quasi-static scenarios, while ignoring the challenging issues (i.e., intermittent visibility, dynamic network configuration, and communication delays) introduced by satellites’ movement and communication protocols enabling the integration of terrestrial and non-terrestrial networks. To bridge this gap, this work presents a network architecture with novel orchestration capabilities of services into non-terrestrial segments. In the proposed approach, the interaction between terrestrial and non-terrestrial entities and the cloud has been detailed across service request, configuration, and provisioning phases. Then, starting from a system model describing the network configuration and the resulting deployment delays of services, an optimization problem has been formulated to dynamically allocate Virtual Network Functions (VNFs) among LEO CubeSats over a looking-ahead horizon, based on service requests, computational capabilities of the involved CubeSats, visibility matrices, and expected deployment delay bounds. Finally, the proposed optimization problem has been solved through three heuristic strategies. Computer simulations have been carried out to demonstrate the ability of the developed strategies to achieve results close to the optimal solution and to ensure better performance against a benchmark scheme.

Joint Virtual Network Function Deployment and Scheduling via Heuristics and Deep Reinforcement Learning

Joint Virtual Network Function Deployment and Scheduling via Heuristics and Deep Reinforcement Learning

Joint VNF Parallelization and Deployment in Mobile Edge Networks

Mobile edge computing (MEC) has emerged as a promising computing paradigm that provides flexible and responsive local services for mobile user equipment at the network edge. Software instances for user equipment tasks are typically deployed as Virtualized Network Functions (VNFs) at resource-constrained edge nodes. Task data exchanged across the VNFs in serial can incur high task completion latency. It is therefore desirable to deploy certain VNFs in parallel. However, deciding where to deploy VNFs depends on which VNFs are parallel, and conversely, their deployment also affects their parallel execution. In this paper, for the first time, we jointly consider the parallelization and deployment strategies for VNFs at edge nodes. We closely examine the complexity of the joint optimization problem and introduce an Improved Service Function Graph (I-SFG) that reflects the coordination and dependency relations among the VNFs to provide parallel services for each piece of user equipment. We first propose an approach based on integer linear programming to find optimal solutions in small-scale scenarios and then present an effective solution through cascading I-SFG construction and VNF deployment approximation to solve large-scale problems. Theoretical analyses and experimental results show the superiority of our joint design and the proposed practical solution.

Group Sparse Space Information Network With Joint Virtual Network Function Deployment and Maximum Flow Routing Strategy

For the space information network (SIN) with network function virtualization (NFV), a large number of active nodes deployed with virtual network functions (VNFs) impose heavy coordination overhead. In this paper, we investigate the trade-off between the network maximum flow and coordination overhead under the service function chain (SFC) constraints. Specifically, we propose the group sparse joint VNFs deployment and flow routing strategy (GS-VNF-R) to strike the trade-off between the network maximum flow and coordination overhead. Although the GS-VNF-R scheme can be formulated as a convex problem, for a large-scale SIN, solving the GS-VNF-R problem by traditional convex optimizations imposes a heavy computation burden. In order to reduce the time complexity, we propose a novel optimal low-complexity block-successive upper-bound minimization method of multipliers based group sparse (BSUM-M-GS) algorithm, which can converge to the global optimal with much less complexity. Simulation results show that for some scenarios, 60% of active nodes can be saved by using the proposed GS-VNF-R scheme without any performance loss compared to the full cooperation scheme, which results in significant cooperation overhead reduction. Moreover, simulation results demonstrate that our proposed BSUM-M-GS algorithm can significantly reduce the complexity to the extent of 7 orders of magnitude for some scenarios.

5G-Kube: Complex Telco Core Infrastructure Deployment Made Low-Cost

Network Function Virtualization (NFV) along with Software Defined Networking (SDN) have brought an evolution in telecommunications laying out the bases for 5G networks and its softwarization. Accordingly, new implementations of telecom standards, such as the 3GPP 5G Core, are defined as fully-virtualized infrastructures consisting of different components and leveraging a cloudnative approach. At the same time, standard-oriented solutions, such as ETSI Management and Orchestration (MANO), have emerged to master the complexity of Virtualized Network Functions (VNFs) orchestration, including 5G Core VNFs. While MANO operates at the NFV level, it also leverages existing cloud infrastructures for the deployment of VNFs by interoperating with resource orchestrators at the cloud level. From the business perspective, that requires telco operators to interact with different technology providers, from NFV/MANO software producers to cloud computing providers, and to hire technicians proficient in the technologies of both telco and computing worlds, that are a rather difficult human resourcing to find. The main claim of the paper is that the Development and Operations (DevOps) tools in the IT world are mature enough to leverage them directly in the telco world, without superimposing other interlaced standard/ software. That allows to significantly reduce OPEX cost of complex telco infrastructures by supporting all needed automation and by avoiding the combined use of (too) complex layered standards/software stacks, such as in the case of MANO. Accordingly, in this paper, we leverage container-based technologies and Kubernetes to design and evaluate a novel deployment approach, called 5G-Kube, for softwarized 5G core networks. 5G-Kube, which is openly to the community, has been also evaluated in two different use cases of the 5G Core and Kubernetes deployment fitting, namely, Industry 4.0 and Smart Cities.

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey.

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

Space Information Network With Joint Virtual Network Function Deployment and Flow Routing Strategy With QoS Constraints

Space information network (SIN) can provide global coverage in 6G network. Furthermore, SIN with network function virtualization (NFV) can achieve flexible deployment of network functions and improve the utilization of resources. In SIN with NFV, network functions can be virtualized into virtual network functions (VNFs). However, in SIN with NFV, the mission flow must satisfy the service function chain (SFC) constraint, i.e., the mission flow must be processed by all VNFs in the predefined order. Furthermore, each VNF can be deployed on multiple physical nodes. Moreover, different kinds of services may have the diverse quality of service (QoS) requirements. In this paper, we investigate the joint VNFs deployment and flow routing strategy (VNF-R) to maximize the number of completed missions with the guaranteed end-to-end latency under SFC constraints in time-varying SINs. Specifically, the problem can be formulated as a mixed integer linear programming (MILP) problem, which is proved to be NP-hard. In order to effectively solve the problem, we propose a novel low-complexity near-optimal penalty successive upper bound minimization rounding LP relaxation iterative rounding (PSUM-R-LRIR) algorithm. The simulation results show that the PSUM-R-LRIR algorithm can achieve near-optimal performance, and our proposed VNF-R scheme significantly outperforms the fixed VNF deployment scheme.

Performance characterization and profiling of chained CPU-bound Virtual Network Functions

The increased demand for high-quality Internet connectivity resulting from the growing number of connected devices and advanced services has put significant strain on telecommunication networks. In response, cutting-edge technologies such as Network Function Virtualization (NFV) and Software Defined Networking (SDN) have been introduced to transform network infrastructure. These innovative solutions offer dynamic, efficient, and easily manageable networks that surpass traditional approaches. To fully realize the benefits of NFV and maintain the performance level of specialized equipment, it is critical to assess the behavior of Virtual Network Functions (VNFs) and the impact of virtualization overhead. This paper delves into understanding how various factors such as resource allocation, consumption, and traffic load impact the performance of VNFs. We aim to provide a detailed analysis of these factors and develop analytical functions to accurately describe their impact. By testing VNFs on different testbeds, we identify the key parameters and trends, and develop models to generalize VNF behavior. Our results highlight the negative impact of resource saturation on performance and identify the CPU as the main bottleneck. We also propose a VNF profiling procedure as a solution to model the observed trends and test more complex VNFs deployment scenarios to evaluate the impact of interconnection, co-location, and NFV infrastructure on performance.

Performance Evaluation of Virtualization Methodologies to Facilitate NFV Deployment

The development of the Next-Generation Wireless Network (NGWN) is becoming a reality. To conduct specialized processes more, rapid network deployment has become essential. Methodologies like Network Function Virtualization (NFV), Software-Defined Networks (SDN), and cloud computing will be crucial in addressing various challenges that 5G networks will face, particularly adaptability, scalability, and reliability. The motivation behind this work is to confirm the function of virtualization and the capabilities offered by various virtualization platforms, including hypervisors, clouds, and containers, which will serve as a guide to dealing with the stimulating environment of 5G. This is particularly crucial when implementing network operations at the edge of 5G networks, where limited resources and prompt user responses are mandatory. Experimental results prove that containers outperform hypervisor-based virtualized infrastructure and cloud platforms’ latency and network throughput at the expense of higher virtualized processor use. In contrast to public clouds, where a set of rules is created to allow only the appropriate traffic, security is still a problem with containers.

Virtual network function deployment algorithm based on graph convolution deep reinforcement learning

Virtual network function deployment algorithm based on graph convolution deep reinforcement learning

Graph neural network‐based virtual network function deployment optimization

Graph neural network‐based virtual network function deployment optimization

A Binary Gray Wolf Optimization algorithm for deployment of Virtual Network Functions in 5G hybrid cloud

A Virtual Network Function (VNF) is responsible for running codes that have been offloaded by mobile users, and are hosted in the cloud and edge servers of the 5G Internet’s hybrid cloud infrastructure. The two key design goals of VNF deployment in a 5G hybrid cloud are to reduce deployment cost and minimize service latency experienced by users (i.e., to maximize their Quality-of-Experiences). However, these two service parameters oppose each other as the reduction of user service latency requires the deployment of a higher number of VNF instances, incurring additional costs. In this work, the aforementioned VNF deployment problem is formulated as a Multi-objective Linear Programming (MOLP) problem that brings a trade-off between the two conflicting objectives. Due to the NP-hardness of the above MOLP framework, we develop an artificial intelligence (AI) driven meta-heuristic Binary Gray Wolf Optimization (BGWO) algorithm for VNF deployment that achieves a near-optimal solution in polynomial time. In comparison to state-of-the-art works, the results of simulated experiments, developed by Python programming version 3.10.2, demonstrate a significant improvement in minimizing VNF deployment costs and maximizing users’ QoE up to 30% and 10%, respectively.

Optimal deployment of virtual network functions for securing telecommunication networks against distributed denial of service attacks: A robust optimization approach

Distributed Denial of Service (DDoS) cyberattacks represent a major security risk for network operators and internet service providers. They thus need to invest in security solutions to protect their network against DDoS attacks. The present work focuses on deploying a network function virtualization based architecture to secure a network against an on-going DDoS attack. We assume that the target, sources and volume of the attack have been identified. However, due to 5G network slicing, the exact routing of the illegitimate flow in the network is not known by the internet service provider. We seek to determine the optimal number and locations of virtual network functions in order to remove all the illegitimate traffic while minimizing the total cost of the activated virtual network functions. We propose a robust optimization framework to solve this problem. The uncertain input parameters correspond to the amount of illegitimate flow on each path connecting an attack source to the target and can take values within a predefined uncertainty set. In order to solve this robust optimization problem, we develop an adversarial approach in which the adversarial sub-problem is solved by a Branch & Price algorithm. The results of our computational experiments, carried out on medium-size randomly generated instances, show that the proposed solution approach is able to provide optimal solutions within short computation times.

Optimization of 5G/6G Telecommunication Infrastructure through an NFV-Based Element Management System

Network Function Virtualization (NFV) is an enabling technology that brings together automated network service management and corresponding virtualized network functions that use an NFV Infrastructure (NFVI) framework. The Virtual Network Function Manager (VNFM) placement in a large-scale distributed NFV deployment is therefore a challenging task due to the potential negative impact on performance and operating expense cost. The VNFM assigns Virtual Network Functions (VNFs) and operates efficiently based on network demands with resilient performance through efficient placement techniques. The degradation in performance and a tremendous increase in capital expenditure and operating expenses indicated this chaotic problem. This article proposed a method for VNFM placement using information on the resources of each nodes’ Element Manager (EM), which is an efficient method to assign VNFs to each node of element management systems. In addition, this paper proposed an Optimized Element Manager (OEM) method for looking at appropriate EMs for the placement of VNF through periodic information on available resources. It also overcomes challenges such as delays and variations in VNFs workload for edge computing and distributed cloud regions. The performance is measured based on computations performed on various optimization algorithms such as linear programming and tabu search algorithms. The advent of the new service provisioning model of BGP-EVPN for VXLAN is materialized by integrating VTS with OpenStack. The numerical analysis shows that the proposed OEM algorithm gives an optimal solution with an average gap of 8%.

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.