NFV-enabled Network Research Articles

Knowledge-Assisted Actor Critic Proximal Policy Optimization-Based Service Function Chain Reconfiguration Algorithm for 6G IoT Scenario.

Future 6G networks will inherit and develop Network Function Virtualization (NFV) architecture. With the NFV-enabled network architecture, it becomes possible to establish different virtual networks within the same infrastructure, create different Virtual Network Functions (VNFs) in different virtual networks, and form Service Function Chains (SFCs) that meet different service requirements through the orderly combination of VNFs. These SFCs can be deployed to physical entities as needed to provide network functions that support different services. To meet the highly dynamic service requirements in the future 6G Internet of Things (IoT) scenario, the highly flexible and efficient SFC reconfiguration algorithm is the key research direction. Deep-learning-based algorithms have shown their advantages in solving this type of dynamic optimization problem. Considering that the efficiency of the traditional Actor Critic (AC) algorithm is limited, the policy does not directly participate in the value function update. In this paper, we use the Proximal Policy Optimization (PPO) clip function to restrict the difference between the new policy and the old policy, to ensure the stability of the updating process. We combine PPO with AC, and further bring the historical decision information as the network knowledge to offer better initial policies, to accelerate the training speed. We also propose the Knowledge = Assisted Actor Critic Proximal Policy Optimization (KA-ACPPO)-based SFC reconfiguration algorithm to ensure the Quality of Service (QoS) of end-to-end services. Simulation results show that the proposed KA-ACPPO algorithm can effectively reduce computing cost and power consumption.

Service Function Chaining to Support Ultra-Low Latency Communication in NFV

Network function virtualization (NFV) has the potential to fundamentally transform conventional network architecture through the decoupling of software from dedicated hardware. The convergence of virtualization and cloud computing technologies has revolutionized the networking landscape, offering a wide range of advantages, including improved flexibility, manageability, and scalability. The importance of network capability in enabling ultra-low latency applications has been greatly amplified in the current era due to the increased demand for emerging services such as autonomous driving, teleoperated driving, virtual reality, and remote surgery. This paper presents a novel and efficient methodology for service function chaining (SFC) in an NFV-enabled network that aims to minimize latency and optimize the utilization of physical network resources, with a specific focus on ultra-low latency applications. In our proposed methodology, we offer flow prioritization and an adjustable priority coefficient factor (µ) to reserve a portion of physical network resources exclusively for ultra-low latency applications in order to optimize the deployment paths of these applications further. We formulate the SFC deployment problem as an integer linear programming (ILP) optimization model. Furthermore, we propose a set of heuristic algorithms that yield near-optimal solutions with minimal optimality gaps and execution times, making them practical for large-scale network topologies. Performance evaluations demonstrate the effectiveness of our proposed methodology in enabling ultra-low latency applications in an NFV-enabled network. Compared to existing algorithms, our proposed methodology achieves notable enhancements in terms of the end-to-end delay (up to 22 percent), bandwidth utilization (up to 28 percent), and SFC acceptance rate (up to 13 percent).

Placement and Allocation of Virtual Network Functions: Multi-Dimensional Case

With the gradual transitioning to Network function virtualization (NFV), Internet service providers face the problem of where to introduce NFV in order to make the most benefit of that; here, we measure the benefit by the amount of traffic that can be served in an NFV-enabled network. This problem is non-trivial as it is composed of two challenging subproblems: 1) placement of nodes to support virtual network functions (referred to as VNF-nodes); 2) allocation of the VNF-nodes' resources to network flows. This problem has been studied for the one-dimensional setting, where all network flows require one network function, which requires a unit of resource to process a unit of flow. In this work, we consider the multi-dimensional setting, which introduces new challenges in addition to those of the one-dimensional setting (e.g., NP-hardness and non-submodularity). To address these difficulties, we propose a novel two-level relaxation method that allows us to draw a connection to the sequence submodular theory and utilize the property of sequence submodularity along with the primal-dual technique to design two approximation algorithms. We prove that the proposed algorithms have a non-trivial approximation ratio and perform trace-driven simulations to show the effectiveness of the proposed algorithms.

APVNFC: Adaptive Placement of Virtual Network Function Chains

Abstract Designing efficient and flexible approaches for placement of Virtual Network Function (VNF) chains is the main success of Network Function Virtualization (NFV). However, most current work considers the constant bandwidth and flow processing requirements while deploying the VNFs in the network. The constant (immutable) flow processing and bandwidth requirements become critical limitations in an NFV-enabled network with highly dynamic traffic flow. Therefore, bandwidth requirements and available resources of the Point-of-Presence (PoP) in the network change constantly. We present an adaptive model for placing VNF chains to overcome this limitation. At the same time, the proposed model minimizes the number of changes (i.e., re-allocation of VNFs) in the network. The experimental evaluation shows that the adaptive model can deliver stable network services. Moreover, it reduces the significant number of changes in the network and ensures flow performance.

Drops on surface optimization (DSO): A new reinforcement learning based metaheuristic algorithm for virtual network functions placement in distributed cloud architectures

With the goal of making the networks more agile, flexible and cost efficient, network function virtualization (NFV) is one of the latest and most promising technologies introduced. This technology is the key block of future internet and plays a critical role in modern networks. Using this paradigm, different network functions can be independently deployed on general purpose hardware instead of residing on dedicated hardware (network appliances in traditional networks). NFV enables faster development and lower costs. As any other new technology, NFV has some emerging challenges along with its benefits. The main research challenge of NFV is the necessity of optimally placing the virtual network functions (VNFs) in an NFV-enabled network. This problem is mainly known as the VNF placement (VNFP) problem in the literature. Due to the nature of network applications, this placement should be scalable for large network sizes. In this paper, the VNFP problem is addressed and a new metaheuristic algorithm is proposed based on the behavior of droplets on a surface, named the Drops on Surface Optimization (DSO) Algorithm. In order to enhance the proposed algorithm by dynamic selection strategy, a reinforcement learning approach (Q learning) is adopted. A comprehensive evaluation is carried out using standard networks, compared with several methods. Simulation results reveal that our proposed algorithm outperforms other state-of-the-art approaches in terms of total end-to-end propagation delay of the placed service chain, cost of placement, number of active servers involved in the placement and scalability.

Online Service Provisioning in NFV-Enabled Networks Using Deep Reinforcement Learning

In this paper, we study a Deep Reinforcement Learning (DRL) based framework for an online end-user service provisioning in a Network Function Virtualization (NFV)-enabled network. We formulate an optimization problem aiming to minimize the cost of network resource utilization. The main challenge is provisioning the online service requests by fulfilling their Quality of Service (QoS) under limited resource availability. Moreover, fulfilling the stochastic service requests in a large network is another challenge that is evaluated in this paper. To solve the formulated optimization problem in an efficient and intelligent manner, we propose a Deep Q-Network for Adaptive Resource allocation (DQN-AR) in NFV-enabled network for function placement and dynamic routing which considers the available network resources as DQN states. Moreover, the service’s characteristics, including the service life time and number of the arrival requests, are modeled by the Uniform and Exponential distribution, respectively. In addition, we evaluate the computational complexity of the proposed method. Numerical results carried out for different ranges of parameters reveal the effectiveness of our framework. In specific, the obtained results show that the average number of admitted requests of the network increases by 7 up to 14% and the network utilization cost decreases by 5 and 20%.

Real-Time Update of Joint SFC and Routing in Software Defined Networks

To meet the ever-increasing demands for high-quality network services, a software defined network (SDN) can support various virtual network functions (VNFs) using virtualization technology. Due to network dynamics, an SDN needs to be updated frequently to optimize various performance objectives, such as load balancing. Most previous solutions first determine a new network configuration ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , target VNF placement and flow routing) based on the current workload, and then update the VNF placement and routing paths of the existing flows. However, due to massive VNF’s state migration and slow update of the flow table, unacceptable update delay may occur, especially in large or frequently changed networks. In this paper, we address the real-time network update, which jointly considers the optimization of the service function chain (SFC) update and the routing update. We propose the delay-satisfied NFV-enabled network update (DSNU) problem, and prove its NP-Hardness. We design an algorithm with bounded approximation factor to solve this problem. To further reduce the delay, we also design an efficient algorithm for the update scheduling. The experimental results show that our method can reduce the network update delay by about 86% compared with the previous network update methods while preserving a similar network performance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the VNF instance load ratio increases by less than 5%.

Resource Aware Routing for Service Function Chains in SDN and NFV-Enabled Network

Owing to the Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chain (SFC) has become a popular service in SDN and NFV-enabled network. However, as the Virtual Network Function (VNF) of each type is generally multi-instance and flows with SFC requests must traverse a series of specified VNFs in predefined orders, it is a challenge for dynamic SFC formation to optimally select VNF instances and construct paths. Moreover, the load balancing and end-to-end delay need to be paid attention to, when routing flows with SFC requests. Additionally, fine-grained scheduling for traffic at flow level needs differentiated routing which should take flow features into consideration. Unfortunately, traditional algorithms cannot fulfill all these requirements. In this paper, we study the Differentiated Routing Problem considering SFC (DRP-SFC) in SDN and NFV-enabled network. We formulate the DRP-SFC as a Binary Integer Programming (BIP) model aiming to minimize the resource consumption costs of flows with SFC requests. Then a novel routing algorithm, Resource Aware Routing Algorithm (RA-RA), is proposed to solve the DRP-SFC. Performance evaluation shows that RA-RA can efficiently solve the DRP-SFC and surpass the performance of other existing algorithms in acceptance rate, throughput, hop count and load balancing.

Resource Management of Service Function Chain in NFV Enabled Network: A Survey

Resource Management of Service Function Chain in NFV Enabled Network: A Survey

Tackling IoT Scalability with 5G NFV-Enabled Network Slicing

With emerging large volume and diverse heterogeneity of Internet of Things (IoT) applications, the one-size-fits-all design of the current 4G networks is no longer adequate to serve various types of IoT applications. Consequently, the concepts of network slicing enabled by Network Function Virtualization (NFV) have been proposed in the upcoming 5G networks. 5G network slicing allows IoT applications of different QoS requirements to be served by different virtual networks. Moreover, these network slices are equipped with scalability that allows them to grow or shrink their instances of Virtual Network Functions (VNFs) when needed. However, all current research only focuses on scalability on a single network slice, which is the scalability at the VNF level only. Such a design will eventually reach the capacity limit of a single slice under stressful incoming traffic, and cause the breakdown of an IoT system. Therefore, we propose a new IoT scalability architecture in this research to provide scalability at the NS level and design a testbed to implement the proposed architecture in order to verify its effectiveness. For evaluation, three systems are compared for their throughput, response time, and CPU utilization under three different types of IoT traffic, including the single slice scaling system, the multiple slices scaling system and the hybrid scaling system where both single slicing and multiple slicing can be simultaneously applied. Due to the balanced tradeoff between slice scalability and resource availability, the hybrid scaling system turns out to perform the best in terms of throughput and response time with medium CPU utilization.

NFV Resource Allocation: a Systematic Review and Taxonomy of VNF Forwarding Graph Embedding

The emergence of Network Functions Virtualisation (NFV) is drastically reshaping the arrangement of network functions. Instead of being built on dedicated hardware (network appliances), network functions are now implemented as software components that run on top of general purpose hardware through virtualisation, namely virtualised network functions (VNFs). From this paradigm-shifting technology arise two problems: (i) how to place VNFs in an NFV-enabled network; and (ii) how to chain these VNFs. These problems are jointly referred to as the VNF forwarding graph embedding (VNF-FGE) problem. Having efficient solutions to the VNF-FGE problem is key to the success of NFV because placing and chaining VNFs automatically and efficiently reduces network and computing resources, thus reducing capital expenditure (CAPEX) and operating expenditure (OPEX). In this work, we systematically review the literature on the VNF-FGE problem. We present a novel taxonomy for the classification and study of proposed solutions to this problem. Research challenges that remain unaddressed are also discussed, providing recommendations for future work.

Robust Online Composition, Routing and NF Placement for NFV-Enabled Services

Network function virtualization (NFV) fosters innovation in the networking field and reduces the complexity involved in managing modern-day conventional networks. Via NFV, the provisioning of a network service becomes more agile, whereby virtual network functions can be instantiated on commodity servers and data centers on demand. Network functions can be either mandatory or best-effort. The former type is strictly necessary for the correctness of a network service, whereas the latter is preferrable yet not necessary. In this paper, we study the online provisioning of NFV-enabled network services. We consider both unicast and multicast NFV-enabled services with multiple mandatory and best-effort NF instances. We propose a primal-dual based online approximation algorithm that allocates both processing and transmission resources to maximize a profit function, subject to resource constraints on physical links and NFV nodes. The online algorithm resembles a joint admission mechanism and an online composition, routing and NF placement framework. The online algorithm is derived from an offline formulation through a primal-dual based analysis. Such analysis offers direct insights and a fundamental understanding on the nature of the profit-maximization problem for NFV-enabled services with multiple resource types.

Reliability-Aware Service Function Chaining With Function Decomposition and Multipath Routing

Network Function Virtualization (NFV) converts network functions executed by costly middleboxes into instances of Virtual Network Functions (VNFs) hosted by industry-standard Physical Machines (PMs). This has proven to be quite an efficient approach when it comes to enabling automated network operations and the elastic provisioning of resources to support heterogeneous services. Today’s revolutionary services impose a remarkably elevated reliability together with ultra-low latency requirements. Therefore, in addition to having highly reliable VNFs, these VNFs have to be optimally placed in such a way to rapidly route traffic among them with the least utilization of bandwidth. Hence, the proper selection of PMs to meet the above-mentioned reliability and delay requirements becomes a remarkably challenging problem. None of the existing publications addressing such a problem concurrently adopts VNF decomposition to enhance the flexibility of the VNFs’ placement and a hybrid routing scheme to achieve an optimal trade-off between the above-mentioned objectives. In this paper, a VNF-decomposition-based backup strategy is proposed together with a delay-aware hybrid multipath routing scheme for enhancing the reliability of NFV-enabled network services while jointly reducing delays these services experience. The problem is formulated as a Mixed Integer Linear Program (MILP) whose resolution yields an optimal VNF placement and traffic routing policy. Next, the delay-aware hybrid shortest path-based heuristic algorithm is proposed to work around the MILP’s complexity. Thorough numerical analysis and simulations are conducted to validate the proposed algorithm and evaluate its performance. Results show that the proposed algorithm outperforms its existing counterparts by 7.53% in terms of computing resource consumption.

Incremental Server Deployment for Software-Defined NFV-Enabled Networks

Network Function Virtualization (NFV) is a new paradigm to enable service innovation through virtualizing traditional network functions. To construct a new NFV-enabled network, there are two critical requirements: minimizing server deployment cost and satisfying switch resource constraints. However, prior work mostly focuses on the server deployment cost, while ignoring the switch resource constraints (e.g., switch's flow-table size). It thus results in a large number of rules on switches and leads to massive control overhead. To address this challenge, we propose an incremental server deployment (INSD) problem for construction of scalable NFV-enabled networks. We prove that the INSD problem is NP-Hard, and there is no polynomial-time algorithm with approximation ratio of (1- ϵ)· ln m, where ϵ is an arbitrarily small value and m is the number of requests in the network. We then present an efficient algorithm with an approximation ratio of 2 · H(q · p), where q is the number of VNF's categories and p is the maximum number of requests through a switch. We evaluate the performance of our algorithm with experiments on physical platform (Pica8), Open vSwitches, and large-scale simulations. Both experimental results and simulation results show high scalability of the proposed algorithm. For example, our solution can reduce the control and rule overhead by about 88% with about 5% additional server deployment, compared with the existing solutions.

Implementation of a real-time network traffic monitoring service with network functions virtualization

The Network Functions Virtualization (NFV) extends the functionality provided by Software-Defined Networking (SDN). It is a virtualization technology that aims to replace the functionality provided by traditional networking hardware using software solutions. Thereby, enabling cheaper and efficient network deployment and management. The use of NFV and SDN is anticipated to enhance the performance of Infrastructure-as-a-Service (IaaS) clouds. However, due to the presence of a large number of network devices in IaaS clouds offering a plethora of networked services, there is need to develop a traffic monitoring system for the efficient network. This paper proposes and validates an extensible SDN and NFV-enabled network traffic monitoring system. Using extensive experiments, we show that the proposed system can closely match the performance of traditional networks at cheaper costs and by adding more flexibility to network management tasks.

Reliability-Aware Service Chaining In Carrier-Grade Softwarized Networks

Network Function Virtualization (NFV) has revolutionized service provisioning in cloud datacenter networks. It enables the complete decoupling of Network Functions (NFs) from the physical hardware middle boxes that network operators deploy for implementing service-specific and strictly ordered NF chains. Precisely, NFV allows for dispatching NFs as instances of plain software called virtual network functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. Nevertheless, NF softwarization introduces processing vulnerability ( e.g. , failures caused by hardware or software, and so on). Since any failure of VNFs could break down an entire service chain, thus interrupting the service, the functionality of an NFV-enabled network will require a higher reliability compared with traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF chain placement and flow routing optimization. In order to guarantee the required reliability, an incremental approach is proposed to determine the number of required VNF backups. Through illustration, it is shown herein that the formulated single path routing model can be easily extended to support resource sharing between adjacent backup VNF instances. This paper advocates the absolute existence of a share-resource-based VNF assignment strategy that is capable of trading off all of the reliability, bandwidth, and computing resources consumption of a given service chain. A heuristic is proposed to work around the complexity of the presently formulated integer linear programming (ILP). Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of this proposed heuristic reflecting its ability to achieve very close results to those obtained through the resolution of the complex ILP within a negligible amount of time. Above and beyond, the proposed resource-sharing-based VNF placement scheme outperforms existing resource-sharing agnostic schemes by 15.6% and 14.7% in terms of bandwidth and CPU utilization respectively.

ApplianceBricks: a scalable network appliance architecture for network functions virtualization

Network Functions Virtualization (NFV ) is an attempt to help operators more effectively manage their networks by implementing traditional network functions embedded in specialized hardware platforms in term of virtualized software instances. But, existing novel network appliances designed for NFV infrastructure are always architected on a general-purpose x86 server, which makes the performance of network functions limited by the hosted single server. To address this challenge, we propose ApplianceBricks, a novel NFV-enable network appliance architecture that is used to explore the way of consolidating multiple physical network functions into a clustered network appliance, which is able to improve the processing capability of NFV-enabled network appliances.