Network Function Virtualization Infrastructure Research Articles

Securing NFV/SDN IoT Using VNFs Over a Compute-Intensive Hardware Resource in NFVI

The Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are network paradigms for flexibly deploying future networks while guaranteeing security service requirements. This work designs Virtual Network Functions (VNFs) through a Compute-Intensive (CI) hardware resource in Network Function Virtualization Infrastructure (NFVI). The proposed NFVI is characterized by a multi-channel cryptosystem, which can be virtualized as a plurality of VNFs, that is, crypto engines, and each crypto engine is logically dedicated to an NFV/SDN Internet of Things (IoT) device, which does not have or has limited security capability owing to resource constraints. To enhance the performance of a cryptosystem, the accelerator circuit is often deeply pipelined and unrolled. However, to fulfill the popular feedback operation modes, the throughput of the pipelined and unrolled cryptosystem that implements a block cipher, say Advanced Encryption Standard (AES), can deteriorate even lower than that without these techniques. To solve this problem, we design a pipelined and unrolled multichannel cryptosystem, which can be integrated into NFVI edge servers, with feedback operation mode for the NFV/SDN IoT. As a result, the combinational logics of a block cipher with feedback can be shared by plenty of IoT devices to enhance the hardware efficiency as well. Moreover, in addition to briefly review several AES designs, the fastest AES design is derived by shortening its critical path to only a logic gate of multiplexer or Exculsive OR (XOR) gate.

Runtime Adaptation Framework for Fulfilling Availability and Continuity Requirements of Network Services

The Network Function Virtualization (NFV) framework is an enabler for the automation of Network Service (NS) management. In the context of NFV, an NS is realized by interconnecting Virtual Network Functions (VNF) using Virtual Links (VL). Availability and continuity are among the important characteristics of an NS. These characteristics depend on the availability of the VNFs and VLs composing the NS, which are usually selected at NS design time. VNFs and VLs utilize the resources of the underlying infrastructure, and their availability (partially) depends on the availability of these resources. To design an NS to fulfill availability and continuity requirements, the availability required from the resources is constrained at design time. However, the characteristics of these resources may change at runtime due to the dynamicity of NFV infrastructure. Thus, impacting the availability of the VNFs and the VLs, which in turn may impact the availability and continuity of the NS. To fulfill these requirements at runtime despite the changes in the infrastructure, the NS should be adapted. In this paper, we propose a framework for the runtime adaptation of NSs that reacts to changes and adapts the NS configuration so that it can fulfill its availability and continuity requirements during the NS lifetime. We also propose a method to develop machine learning models that are used within the framework to determine the required adjustments at runtime. We implemented the proposed framework, the method for developing the machine learning models, a testbed, and NSs to assess the feasibility and validity of our approach through experiments.

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.

A Multiple Objective Resource Scheduling Algorithm for Network Services on the NFV Infrastructure

Capital expenditure (CAPEX) and operating expenses (OPEX) a key practical concerns for telecommunication companies, emphasizing the attraction of Network Function Virtualization (NFV) in recent years. However, the NFV Infrastructure (NFVI) includes a wide variety of resource types and stakeholders. While many attempts have been made to enhance the ability of existing resource scheduling approaches to support the complex and conflicting NFV ecosystem, they are no longer sufficient. In this paper, I propose the Multiple Objective Resource Scheduling Algorithm (MORSA) to optimize the NFVI resource usage. In developing MORSA, I designed a plug-in architecture to satisfy various requirements related to NFVI resources and stakeholder policies. MORSA allows the NFVI Resource Scheduler to optimize, simultaneously, any combination of possibly conflicting objectives with multifaceted constraints in complex real world situations. Evaluation results show that MORSA obtains good approximate solutions by resolving conflicting objectives in reasonable computation times.

Optimized Multi-Service Tasks Offloading for Federated Learning in Edge Virtualization

Edge federated learning (EFL) utilizes edge computing (EC) to alleviate direct round communications of multi-dimensional model updates between local participants and the central parameter server. With an elastic edge-assisted aggregation procedure, the burden of backbone congestion can be handled for multi-service model communications. To provide an adaptive virtual resource orchestration, the integration of software-defined networking (SDN), network functions virtualization (NFV), EC, and double deep q-networks is emerged, which accurately gathers the state observation from local and edge nodes in data plane and NFV infrastructure (NFVI) for applying actions of allocated resource predetermination and recommended offloading policy. The reward formulation considers the minimization of completion time, energy consumption, and round communications between local participants and the selected NFV-enabled EC (NFVeEC) node until the global model is constructed with satisfying accuracy. In this paper, we proposed a decentralized SDN controller as an agent to enable a softwarization mechanism and interact with the virtualization-based EFL environment. The architecture adoption, agent controller, and orchestrator allow the centralized view for virtual network functions placement with efficient virtual machine mapping to perform edge update procedures and create the forwarding path effectively. The recommended NFVeEC for local-EFL updates is based on reward output and proposed agent valuation.

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%.

An experimental study on latency-aware and self-adaptive service chaining orchestration in distributed NFV and SDN infrastructures

Network Function Virtualization (NFV) and Software Defined Networking (SDN) changed radically the way 5G networks will be deployed and services will be delivered to vertical applications (i.e., through dynamic chaining of virtualized functions deployed in distributed clouds to best address latency requirements). In this work, we present a service chaining orchestration system, namely LASH-5G, running on top of an experimental set-up that reproduces a typical 5G network deployment with virtualized functions in geographically distributed edge clouds. LASH-5G is built upon a joint integration effort among different orchestration solutions and cloud deployments and aims at providing latency-aware, adaptive and reliable service chaining orchestration across clouds and network resource domains interconnected through SDN. In this paper, we provide details on how this orchestration system has been deployed and it is operated on top of the experimentation infrastructure provided within the Fed4FIRE+ facility and we present performance results assessing the effectiveness of the proposed orchestration approach.

NFV-Based Scheme for Effective Protection against Bot Attacks in AI-Enabled IoT

The Internet of Things (IoT) is the upcoming network that aspires to interconnect “things” to each other and to the Internet. Many such “things” like smart devices and wireless sensors are already connected to the Internet, improving human life worldwide. These things can also have artificial intelligence, providing many capabilities to their users. However, such IoT-based devices hide risks since they are usually small devices with constrained resources, and hence do not have sufficient built-in security mechanisms. In addition, the increase of such devices with geometric regression worries network administrators who must take countermeasures to restrict and eliminate attackers who aim to turn the IoT into a Botnet of Things network, using compromised devices as bots to unleash distributed-denial-of-service and man-in-the-middle attacks, or/and spread various types of malware. Thus, they can have unauthorized access and steal very sensitive data from users for malicious purposes. In this article, we highlight the problem caused by the uncontrolled development of insecure IoT-based devices and describe an effective network functions virtualization infrastructure in combination with emerging technologies that could provide smart management and enhanced protection against botnet attacks.

An efficient virtualized network function deployment scheme for service function chain using deep Q‐network

SummaryVirtualized network functions (VNFs) are virtualized network services running on the network functions virtualization infrastructure in order to replace physical hardware. Integrating with software defined networking and network functions virtualization, internet service providers (ISPs) can chain VNFs together, called service function chain (SFC), to provide various network service demands with virtual links. Many studies have proposed for the VNFs deployment under different objectives, for example, energy, delay, quality of service (QoS), and quality of experience (QoE). From the aspects of both ISPs and customers, both energy consumption and QoE are the primary concerns for the purpose of operating expense reduction and users' satisfaction. This paper proposes a QoS/QoE/energy‐aware deep Q‐network (DQN)‐based scheme, called DQN‐QQE for VNFs deployment under the consideration of both energy consumption and QoE requirement with QoS constraints. The reward of the proposed scheme is formulated by the Weber‐Fechner law and the exponential interdependency of QoE and QoS (IQX) hypothesis. Afterwards, we compared the proposed DQN‐QQE to the QoS/QoE‐aware approach DQN‐Q2‐SFC, brute force, and randomness in terms of energy, QoE, error rate, and processing time. The simulation results revealed that DQN‐QQE is more stable and is better than DQN‐Q2‐SFC and randomness approach in terms of energy, QoE, and error rate. The average processing time and energy consumption of the proposed DQN‐QQE was 43% and 11% less than DQN‐Q2‐SFC, respectively. Although brute force approach is better than others in terms of energy, QoE, and error rate, the processing time of brute force approach is nearly 20 times larger than the proposed DQN‐QQE.

Multi-Agent Deep Q-Networks for Efficient Edge Federated Learning Communications in Software-Defined IoT

Federated learning (FL) activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging processes. However, in large-scale heterogeneous Internet of Things (IoT) cellular networks, massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled significantly. This paper introduces the system model of converging software-defined networking (SDN) and network functions virtualization (NFV) to enable device/resource abstractions and provide NFV-enabled edge FL (eFL) aggregation servers for advancing automation and controllability. Multi-agent deep Q-networks (MADQNs) target to enforce a self-learning softwarization, optimize resource allocation policies, and advocate computation offloading decisions. With gathered network conditions and resource states, the proposed agent aims to explore various actions for estimating expected long-term rewards in a particular state observation. In exploration phase, optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value selections. Action-based virtual network functions (VNF) forwarding graph (VNFFG) is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure (NFVI). The proposed scheme indicates deficient allocation actions, modifies the VNF backup instances, and reallocates the virtual resource for exploitation phase. Deep neural network (DNN) is used as a value function approximator, and epsilon-greedy algorithm balances exploration and exploitation. The scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term policy. Simulation results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service (QoS) performance metrics, including packet drop ratio, packet drop counts, packet delivery ratio, delay, and throughput.

Optimizing Service Function Chaining Migration With Explicit Dynamic Path

Network Function Virtualization (NFV) can support customized on- demand network services with flexibility and cost-efficiency. Virtual Network Function (VNF) instances need to be scaled out, scaled in, and reallocated across the NFV infrastructure (NFVI) to avoid a violation of service agreements when the demand traffic changes. However, selecting the new placement of VNFs for migrating a service function chain (SFC) is an issue of efficient NFV control. We propose two novel integer linear programming (ILP) models and two approximation algorithms for SFC placement and migration to maximize the cost-efficiency of an NFV network regarding the changes of service demands and dynamic routing. The ILP models allow us to obtain the optimal solutions of SFC placement and migration with explicit dynamic paths. The approximation migration results provided by our proposed heuristic and reinforcement learning algorithms are close to the optimal solution. Evaluation results carried out with real datasets and synthetic network topologies provide a helpful suggestion of a migration strategy for an NFV service provider to optimize the operating cost of an NFV network in the long term.

A power-efficient and performance-aware online virtual network function placement in SDN/NFV-enabled networks

Recent development of software-defined networks (SDN) and network function virtualization (NFV), makes it feasible to replace dedicated hardware middleboxes with software virtualization to run network functions on general-purpose servers. The main challenge in the NFV is virtual network function placement (VNF-P) which is an NP-hard problem, and thus, heuristics and metaheuristics can be used to solve it. In this paper, a hybrid heuristic-metaheuristic learning model is introduced for online VNF-P. In this model, a multi-criteria heuristic (named MCH) is utilized for online VNF placement and routing, while a metaheuristic based on genetic algorithm (GA) is applied in an offline procedure to learn the hyperparameters of the online MCH model, aims to minimize the total power consumption in the NFV infrastructure. The optimization procedure using GA is performed only once before the main operation of the NFV. Simulation results demonstrate the effectiveness of the MCH-GA learning model against the existing methods.

Virtual Network Resource Optimization Model for Network Function Virtualization

Network function virtualization (NFV) is a concept aimed at achieving telecom grade cloud ecosystem for new-generation networks focusing on capital and operational expenditure (CAPEX and OPEX) savings. This study introduces empirical throughput prediction model for the virtual network function (VNF) and network function virtualization infrastructure (NFVI) architectures based on Linux kernel. The model arises from the methodology for performance evaluation and modeling based on execution area (EA) distribution by CPU core pinning. EA is defined as a software execution unit that can run isolated on a compute resource (CPU core). EAs are derived from the elements and packet processing principles in NFVIs and VNFs based on Linux kernel. Performing measurements and observing linearity of the measured results open the possibility to apply model calibration technique to achieve general VNF and NFVI architecture model with performance prediction and environment setup optimization. The modeling parameters are derived from the cumulative packet processing cost obtained by measurements for collocated EAs on the CPU core hosting the bottleneck EA. The VNF and NFVI architecture model with performance prediction is successfully validated against the measurement results obtained in emulated environment and used to predict optimal system configurations and maximal throughput results for different CPUs.

Editorial for Wiley's IJSCN Special Issue “Satellite networks integration with 5G”

Editorial for Wiley's IJSCN Special Issue “Satellite networks integration with 5G”

Elastic Provisioning of Stateful Telco Services in Mobile Cloud Networking

Several relevant research and innovation activities have recently investigated the technical and economic advantages of cloud computing for the provisioning of telco service infrastructures, in particular towards all-IP next generation 5G networks. In fact, the evolution of telco service infrastructures traditionally requires a significant upfront investment (and a long adoption process). Conversely, cloud exploitation significantly lowers investment risks by potentially providing elasticity in service provisioning via flexible Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) Infrastructure. In this context, the paper presents novel solutions that we have designed, implemented, and evaluated within the EU FP7 Mobile Cloud Networking project (MCN). Their aim is to achieve cost-effective elastic provisioning of telco services over heterogeneous and federated cloud providers, with the specific focus of supporting the extreme quality levels that are demanded by traditional, non-virtualized, and dedicated telco infrastructures. In particular, we concentrate on how to effectively and efficiently automate service state migration for coarse-grained telco service (cloudified) components by leveraging industry-mature orchestration technologies and cloud management frameworks. While our proposed state migration model and procedure are general, its implementation is experimented for MCN's Rating, Charging, and Billing as a Service (RCBaaS). This MCN functionality has been chosen by purpose due to its challenging reliability and uptime requirements. The reported experimental and simulation results show the technical feasibility of the proposed solution under different and realistic load conditions for next-generation and cloudified 5G services.

Using Aerial and Vehicular NFV Infrastructures to Agilely Create Vertical Services.

5G communications have become an enabler for the creation of new and more complex networking scenarios, bringing together different vertical ecosystems. Such behavior has been fostered by the network function virtualization (NFV) concept, where the orchestration and virtualization capabilities allow the possibility of dynamically supplying network resources according to its needs. Nevertheless, the integration and performance of heterogeneous network environments, each one supported by a different provider, and with specific characteristics and requirements, in a single NFV framework is not straightforward. In this work we propose an NFV-based framework capable of supporting the flexible, cost-effective deployment of vertical services, through the integration of two distinguished mobile environments and their networks: small sized unmanned aerial vehicles (SUAVs), supporting a flying ad hoc network (FANET) and vehicles, promoting a vehicular ad hoc network (VANET). In this context, a use case involving the public safety vertical will be used as an illustrative example to showcase the potential of this framework. This work also includes the technical implementation details of the framework proposed, allowing to analyse and discuss the delays on the network services deployment process. The results show that the deployment times can be significantly reduced through a distributed VNF configuration function based on the publish–subscribe model.

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem.

Network Function Virtualization (NFV) has been regarded as one of the key enablers for the 5th Generation of mobile networks, or 5G. This paradigm allows to reduce the dependence on specialized hardware to deploy telecommunications and vertical services. To this purpose, it relies on virtualization techniques to softwarize network functions, simplifying their development and reducing deployment time and costs. In this context, Universidad Carlos III de Madrid, Telefónica, and IMDEA Networks Institute have developed an NFV ecosystem inside 5TONIC, an open network innovation center focused on 5G technologies, enabling the creation of complex, close to reality experimentation scenarios across a distributed set of NFV infrastructures, which can be made available by stakeholders at different geographic locations. This article presents the protocol that has been defined to incorporate new remote NFV sites into the multi-site NFV ecosystem based on 5TONIC, describing the requirements for both the existing and the newly incorporated infrastructures, their connectivity through an overlay network architecture, and the steps necessary for the inclusion of new sites. The protocol is exemplified through the incorporation of an external site to the 5TONIC NFV ecosystem. Afterwards, the protocol details the verification steps required to validate a successful site integration. These include the deployment of a multi-site vertical service using a remote NFV infrastructure with Small Unmanned Aerial Vehicles (SUAVs). This serves to showcase the potential of the protocol to enable distributed experimentation scenarios.

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Network Function Virtualization (NFV) has been regarded as one of the key enablers for the 5th Generation of mobile networks, or 5G. This paradigm allows to reduce the dependence on specialized hardware to deploy telecommunications and vertical services. To this purpose, it relies on virtualization techniques to softwarize network functions, simplifying their development and reducing deployment time and costs. In this context, Universidad Carlos III de Madrid, Telefónica, and IMDEA Networks Institute have developed an NFV ecosystem inside 5TONIC, an open network innovation center focused on 5G technologies, enabling the creation of complex, close to reality experimentation scenarios across a distributed set of NFV infrastructures, which can be made available by stakeholders at different geographic locations. This article presents the protocol that has been defined to incorporate new remote NFV sites into the multi-site NFV ecosystem based on 5TONIC, describing the requirements for both the existing and the newly incorporated infrastructures, their connectivity through an overlay network architecture, and the steps necessary for the inclusion of new sites. The protocol is exemplified through the incorporation of an external site to the 5TONIC NFV ecosystem. Afterwards, the protocol details the verification steps required to validate a successful site integration. These include the deployment of a multi-site vertical service using a remote NFV infrastructure with Small Unmanned Aerial Vehicles (SUAVs). This serves to showcase the potential of the protocol to enable distributed experimentation scenarios.

Virtual IoT Service Slice Functions for Multiaccess Edge Computing Platform

With the advancement of the Internet of Things (IoT), different use cases, such as smart factories, smart cities, and smart cars are being developed. Technologies, such as multiaccess edge computing (MEC) and network slicing are being researched to support various vertical use cases. However, simply utilizing network slicing and MEC is insufficient when providing a specific IoT service. Accordingly, there is a need for a method of arranging and operating common service functions (CSFs) used in the IoT platform at the edge of the network. In addition, it is necessary not only to deliver virtual CSFs but also to implement autoscaling policies of these virtual common service resources. This study extends the idea of resource slicing technologies for the IoT service layer. The IoT service layer exposes the virtual IoT CSFs as an IoT slice service to be provided in the form of virtual network functions (VNFs) with MEC applications on top of the network function virtualization infrastructure (NFVI) at the edge of the network. We propose a framework architecture for virtual IoT slice service orchestration, and illustrate how to instantiate the virtual IoT service functions in the existing centralized cloud to the MEC platform. We also propose an elastic computing algorithm of virtual IoT slice services functions(vIoT-SSFs) resources at the NFVI so that the IoT applications and underlying IoT resources can access vIoT-SSFs at the edge of the network.

Algorithms for functionalities of virtual network: a survey

Nowadays, network functionalities are provided through dedicated hardware middleboxes. These hardware middleboxes are statically embedded with network topology to provide the network services. The types of middleboxes increase in the network, with an increased number of users and network services. The growth in multiple kinds of middleboxes creates an ossified network that is difficult for network management. Network Function Virtualization (NFV) deals with such problems by eliminating network functions from the piece of hardware and implement it as a software to execute in a commodity server. The network virtualization provides more flexibility and elasticity in the network to automatically manage and control the network functions. NFV internally implements the different algorithms to automate the on-demand service provisioning mechanism. In the service provisioning process, NFV allows to share the physical resources among the Virtual Network Functions (VNFs). In order to optimally allocate the physical resources to each VNF, there is a need to include some efficient resource sharing mechanisms in NFV infrastructure. These resource sharing mechanisms are the VNF placement, service chaining, VNFs scheduling, and VNF migration. Therefore, this survey discusses the solutions, implementation tools, and limitations with respect to these resource sharing mechanisms. Moreover, research challenges and current trends related to the resource sharing strategies are explored.