Deployment Of Network Functions Research Articles

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.

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

A Link-Layer Virtual Networking Solution for Cloud-Native Network Function Virtualisation Ecosystems: L2S-M

Microservices have become promising candidates for the deployment of network and vertical functions in the fifth generation of mobile networks. However, microservice platforms like Kubernetes use a flat networking approach towards the connectivity of virtualised workloads, which prevents the deployment of network functions on isolated network segments (for example, the components of an IP Telephony system or a content distribution network). This paper presents L2S-M, a solution that enables the connectivity of Kubernetes microservices over isolated link-layer virtual networks, regardless of the compute nodes where workloads are actually deployed. L2S-M uses software-defined networking (SDN) to fulfil this purpose. Furthermore, the L2S-M design is flexible to support the connectivity of Kubernetes workloads across different Kubernetes clusters. We validate the functional behaviour of our solution in a moderately complex Smart Campus scenario, where L2S-M is used to deploy a content distribution network, showing its potential for the deployment of network services in distributed and heterogeneous environments.

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.

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.

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 Novel 5G-advanced Core Network Intelligent Operation and Maintenance System

The 5G core network adopts Network Functions Virtualization technology, which forms a variety of virtual resources for general computing, storage, and network hardware to achieve dynamic and flexible deployment of network functions and resources based on needs. With large-scale commercial deployment of cloud-native 5G core network with network function virtualization technology, the operation and maintenance of 5G core network is facing new challenges. This paper describes the architecture and deployment characteristics of 5G core network, and analyses the challenges brought to operation and maintenance by new network characteristics such as SBA, NFV, MEC and separated UPF, as well as the difficulties of operation and maintenance of virtual deployed 5G core network. Combined with the architecture evolution and function enhancement of 5G-Advanced, the architecture and scheme of A Novel Intelligent 5G-Advanced Core Network Operation and Maintenance System are proposed. The architecture and scheme of 5G core network operation and maintenance system construction were proposed, and suggestions on the organizational structure of 5G core network cloud-network integration operation and maintenance were put forward, so as to provide reference for 5G core network cloud-network integration operation and maintenance. The proposed scheme improves the performance of operation and maintenance of 5G core network significantly.

Design and Analysis of Dynamic Block-setup Reservation Algorithm for 5G Network Slicing

In 5G, network functions can be scaled out/in dynamically to adjust the capacity for network slices. The scale-out/-in procedure, namely autoscaling, enhances performance by scaling out instances and reduces operational costs by scaling in instances. However, the autoscaling problems in 5G networks are different from those in traditional cloud computing. The 5G network functions must be considered the simultaneous deployment of multiple instances; moreover, the deployment of 5G network functions is more frequent than that of traditional cloud computing. Both the number and timing of deployment will substantially affect the cost-effectiveness of the system. In this paper, we first identify the autoscaling issues specifically based on the 3GPP standards. We develop a low-complexity analytical queuing model to formulate the problem and quantify a set of performance metrics with closed-form solutions. The proposed analytical model and closed-form solutions are cross-validated by extensive simulations. The analytical model offers design insights and theoretical guidelines, helping us study the effectiveness of reservations. We proposed a dynamic block-setup reservation algorithm (DBRA) to find the optimal reserved number and threshold value of network slices. Therefore, mobile operators can balance the system's cost-effectiveness without large-scaled testing and real deployment, saving cost on time and money.

Dynamic Network Security Function Enforcement via Joint Flow and Function Scheduling

Network Function Virtualization (NFV) is a new networking paradigm to enable dynamic network function deployment in networks. Existing studies focused on optimized function deployment and management in NFV. Unfortunately, these studies did not well address the problem of efficient security function enforcement in networks, which is the goal of deploying network functions (NFs), i.e., for real-time security function enforcement on the traffic, since optimal function deployment does not mean efficient security function enforcement on network traffic. In particular, they incurred significant NF enforcement cost. In order to address this issue, in this paper, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\textsf {FuncE}}$ </tex-math></inline-formula> that aims to solve the efficient real-time security function enforcement problem by developing unified dynamic flow and function scheduling. We formulate the problem as an integer linear programming problem and prove that it is NP-hard. We tackle the problem by decomposing it and developing heuristics to achieve near-optimal solutions. We conduct comprehensive experiments by using real topologies to demonstrate the effectiveness of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\textsf {FuncE}}$ </tex-math></inline-formula> design. The experimental results demonstrate that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\textsf {FuncE}}$ </tex-math></inline-formula> achieves near-optimal network function enforcement, which incurs over 100 times less latency than the existing the optimal solver. In particular, compared to the state-of-art defenses, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\textsf {FuncE}}$ </tex-math></inline-formula> processes the same number of candidate flows using over 50% less VNFs, while ensuring the same level of function enforcement.

Building In-the-Cloud Network Functions: Security and Privacy Challenges

Network function virtualization (NFV) has been promising to improve the availability, programmability, and flexibility of network function deployment and communication facilities. Meanwhile, with the advancements of cloud technologies, there has been a trend to outsource network functions through virtualization to a cloud service provider, so as to alleviate the local burdens on provisioning and managing such hardware resources. Promising as it is, redirecting the communication traffic to a third-party service provider has drawn various security and privacy concerns. Traditional end-to-end encryption can protect the traffic in transmit, but it also hinders data usability. This dilemma has raised wide interests from both industry and academia, and great efforts have been made to realize privacy-preserving network function outsourcing that can guarantee the confidentiality of network communications while preserving the ability to inspect the traffic. In this article, we conduct a comprehensive survey of the state-of-the-art literature on network function outsourcing, with a special focus on privacy and security issues. We first give a brief introduction to NFV and pinpoint its challenges and security risks in the cloud context. Then, we present detailed descriptions and comparisons of existing secure network function outsourcing schemes in terms of functionality, efficiency, and security. Finally, we conclude by discussing possible future research directions.

A network operator-biased approach for multi-service network function placement in a 5G network slicing architecture

The 5G communication standard is characterized by an increased softwarization, allowing a higher flexibility able to cope with different requirements and services. In particular, Network Function Virtualization (NFV) is a recently introduced technology that enables a software implementation of different network functions exploiting virtualization techniques, hence, enabling their flexible deployment upon system requirements. Boosted by NFV, the concept of network slicing is gaining great attention in 5G networks. The idea is that physical communication and computing resources are sliced in multiple end-to-end logical networks, each one tailored to best support a specific service. The advantages of NFV, in the network slicing context, are even more evident in distributed computing environments, such as the edge-to-cloud continuum, recently introduced for enabling a flexible deployment of multiple functions. In particular, thanks to the introduction of cloud-native technologies, based on the usage of containerization and microservice technologies, the virtual network functions (VNFs) deployment and their orchestration is an easy operation, allowing the on-the-fly network configuration. Gaining from the NFV, Network Slicing and Edge-to-Cloud continuum paradigms, we propose a new network function allocation problem for multi-service 5G networks, able to deploy network functions on a distributed computing environment depending on the service requests. The proposed approach jointly considers Radio Access Network (RAN) and Core Network (CN) functions and, differently from other approaches, introduces an option able to bias the function placement depending on the service requirements, allowing a fast-and-easy operator-side deployment of the network functions. We propose to solve the problem through a Genetic Algorithm able to approach the optimal solution but with reduced complexity and execution time. The performance is compared with two other heuristic algorithms and with an exhaustive search algorithm, introduced as benchmarks, showing the benefits of the selected solution in terms of performance, flexibility and complexity.

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.

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.

A Survey of Computational Intelligence for 6G: Key Technologies, Applications and Trends

The ongoing deployment of 5G network involves the Internet of Things (IoT) as a new technology for the development of mobile communication, where the Internet of Everything (IoE) as the expansion of IoT has catalyzed the explosion of data and can trigger new eras. However, the fundamental and key component of the IoE depends on the computational intelligence (CI), which may be utilized in the sixth generation mobile communication system (6G). The motivation of this article presents the 6G enabled network in box (NIB) architecture as a powerful integrated solution that can support comprehensive network management and operations. The 6G enabled NIB can be used as an alternative method to meet the needs of next-generation mobile networks by dynamically reconfiguring the deployment of network functions, providing a high degree of flexibility for connection services in various situations. Especially the CI technology such as evolutionary computing, neural computing and fuzzy systems utilized as a part of NIB have inherent capabilities to handle various uncertainties, which have unique advantages in processing the variability and diversity of large amounts of data. Finally, CI technology for NIB, which is widely used is also introduced such as distributed computing, fog computing, and mobile edge computing in order to achieve different levels of sustainable computing infrastructure. This article discusses the key technologies, advantages, industrial scenario applications of CI technology as NIB, typical use cases and development trends based on IoE, which provides directional guidance for the development of CI technology as NIB for 6G.

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.

SDN-NFV-Aided Edge-Cloud Interplay for 5G-Envisioned Energy Internet Ecosystem

Energy Internet (also referred to as Smart Grid 2.0) is another promising application of the Industrial Internet of Things (IIoT), for example, in the way energy is being produced, traded, distributed, and consumed. This is partly due to the lowering of barriers (e.g., costs and Internet connectivity) and advances in the underlying technologies, such as smart meters, electric vehicles, and actuators. This has also resulted in significant growth in the volume, velocity, variety, veracity, and value of data (i.e., the 5 Vs of big data). However, efficiently and effectively handling such big data remains challenging. One solution currently being explored in the literature (including industry) to cope with the increasing network traffic is to use conventional cloud infrastructure, but the key limitations of such an approach include long response time and high bandwidth consumption. Therefore, there have been attempts to introduce next-generation Internet of Things networks to satisfy (real-time) network service demands and guarantee quality of service, for example, by pushing computing capabilities closer to the users (e.g., edge of the network). However, computation-intensive energy analytics can be challenging to perform at the network edge by edge or fog computing devices. Hence, there have also been attempts to utilize software defined networking (SDN) and network function virtualization (NFV) in order to improve network functionality while adding programmability and flexibility features to the network infrastructure. Specifically, NFV facilitates virtual network function (VNF) deployment and orchestration, while SDN controls them for specific use cases. However, with the rise of next-generation mobile networks (i.e., 5G), applications and services require fast and smooth operations with greater flexibility, efficiency, and scalability. In order to align with 5G and leverage potential benefits of edge computing, VNFs should possess critical processing requirements (e.g., high throughput, low latency, and minimal computation overheads). In other words, virtualization plays an important role. To date, several techniques have been introduced in order to achieve the desired objective using virtual machines (VMs) and containers in isolation. However, a hybrid approach using VMs and containers is likely to bring potential benefits for the large-scale deployment of VNFs across the heterogeneous edge and cloud platform. Thus, in this article, a novel architecture for SDN integrated with NFV, specifically for the Energy Internet ecosystem, is presented by leveraging the advantagesof edge computing. In the considered setup, the deployment of VNFs is achieved using a mix of both virtualization and containerization. Findings from our evaluation demonstrate the potential for VNF placement across a hybrid execution setup powered by VMs, as well as the benefits of using containers.

Joint Admission Control and Resource Allocation With Parallel VNF Processing for Time-Constrained Chains of Virtual Network Functions

Network Function Virtualization decouples network function deployment from dedicated hardware and reduces costs. Network services are structured as chains of VNFs. Each chain is a set of VNFs that should be executed according to a predefined order. For some applications, VNF chains should be executed within time constraints to meet the application’s objectives. Most studies provide a solution to allocate substrate network resources to the chains without considering admission control. Allocating resources to all chains may not be possible due to resource limitations. Efficient admission control is therefore required to determine chains admission. This paper proposes a joint admission control and resource allocation mechanism for VNF chains. We propose a resource allocation mechanism based on the idea of parallel VNF processing to meet tight time constraints. As the used assumptions in deterministic modeling of the system do not hold in a wide range of network conditions, we propose a stochastic modeling at which VNF chain execution is modeled by a Queue network. The Queue network is analyzed to calculate the expected value of the probability of deadline meeting in chains, according which the joint resource allocation and admission control problem is modeled as a non-linear optimization. The proposed optimization framework maximizes the profit of the network provider while keeping the confidence level of deadline-meeting for the admitted chains at desired levels. To have an efficient power usage, power consumption is also considered in network provider profit calculation. A heuristic for the joint resource allocation and admission control of VNF chains is proposed. The effectiveness of the proposed method is demonstrated through simulation.

Affinity-Aware VNF Placement in Mobile Edge Clouds via Leveraging GPUs

Mobile edge computing becomes a promising technology to mitigate the latency of various cloud services. In addition, network function virtualization (NFV) has been shown a great potential in reducing the operational cost of cloud services while enhancing the flexibility of virtual network function deployments, by implementing dedicated hardware network functions as pieces of software in generic servers. Recently, the GPU acceleration has been investigated to speed up flow processing in virtual network functions (VNFs), by leveraging the parallelism of GPUs. VNFs that need accelerations prefer to stay at cloudlets (locations) equipped with GPUs. However, little attention has been paid for the VNF placement that takes into account GPU-affinity in cloudlets of mobile edge clouds. In this paper, we consider the affinity-aware throughput maximization problem in a mobile edge cloud via leveraging the parallelism on GPUs for user requests with VNF requirements. We consider two types of affinities in the VNF placement: The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">soft-affinity</i> that allows VNFs to be executed by either CPUs or GPUs in cloudlets; and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hard-affinity</i> that only allows VNFs to be placed to the GPUs of a specified set of cloudlets. We formulate two corresponding VNF placement problems in a mobile edge cloud. Specifically, we first propose an exact solution to the soft-affinity throughput maximization problem by formulating an Integer Linear Program (ILP). We then propose an efficient algorithm for the problem, by proposing a randomized algorithm with a provable approximation ratio for the hard-affinity-aware throughput maximization problem and extending the proposed approximation algorithm to the soft-affinity throughput maximization problem. Furthermore, assuming that user requests arrive into the mobile edge cloud one by one without the knowledge of future arrivals, we devise an online algorithm with a good competitive ratio for this dynamic hard-affinity-aware throughput maximization problem. Finally, we evaluate the performance of the proposed algorithms, through simulations and implementations in a real test-bed. Experimental results show that the performance of the proposed algorithms outperform their existing counterparts and achieve higher throughput.

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.