Virtual Network Functions Placement Problem Research Articles

Virtual Network Function Placement Model Considering Both Availability and Probabilistic Protection for Service Delay

This paper proposes a virtual network function (VNF) placement model considering both availability and probabilistic protection for the service delay to minimize the service deployment cost. Both availability and service delay are key requirements of services; a service provider handles the VNF placement problem with the goal of minimizing the service deployment cost while meeting these and other requirements. The previousworks do not consider the delay of each route which the service can take when considering both availability and delay in the VNF placement problem; only the maximum delay was considered. We introduce probabilistic protection for service delay to minimize the service deployment cost with availability. The proposed model considers that the probability that the service delay, which consists of networking delay between hosts and processing delay in each VNF, exceeds its threshold is constrained within a given value; it also considers that the availability is constrained within a given value. We develop a two-stage heuristic algorithm to solve the VNF placement problem; it decides primary VNF placement by solving mixed-integer second-order cone programming in the first stage and backup VNF placement in the second stage. We observe that the proposed model reduces the service deployment cost compared to a baseline that considers the maximum delay by up to 12%, and that it obtains a feasible solution while the baseline does not in some examined situations.

Placement of virtual network functions for network services

SummaryNetwork function virtualization (NFV) is a name of technology for replacing hardware‐based network functions with software programs. Virtual network function (VNF) is a software program that replaces the hardware‐based network functionality. The replacement of the hardware‐based network functions (middleboxes) with software programs promises the on‐demand provisioning of network functions and reduces capital and operational expenses of the network. Due to this replaced network can adapt to the different network functions. Network service providers deploy various network services with different objectives, such as reducing the network's active servers and traffic latency or network operational expenses. In this article, a VNF placement problem is studied to optimize the total operating costs of the networks. To solve the VNF placement problem, we proposed an integer linear program (ILP) model, which has been implemented using CPLEX. Although an ILP‐based approach gives an optimal solution, it takes a long execution time to find the solution. Due to the long execution time, the ILP‐based approach is not suitable for the real‐time VNF placement problem. To address this challenge, we proposed a heuristic based on dynamic programming that performs better than the existing approaches. The simulation results of the proposed solution using real‐world topologies show that the heuristic approach finds a feasible solution that is only 1 to 1.34 times far from the optimal one. Moreover, experimental results show that the proposed heuristic is 15 to 423 times faster than the ILP.

Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing

The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different QoS requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and Service Function Chaining (SFC) routing problem. However, those prior approaches mainly assume that the network state is static and known, disregarding dynamic network variations. To bridge that gap, we leverage Markov Decision Process (MDP) to model the dynamic network state transitions. To jointly minimize the delay and cost of NFV providers and maximize the revenue, we first devise a customized Deep Reinforcement Learning (DRL) algorithm for the VNF placement problem. The algorithm uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). We then propose attention mechanism-based DRL algorithm for the SFC routing problem, which is to find the path to deliver traffic for the VNFs placed on different nodes. The simulation results show that our proposed algorithms outperform the state-of-the-art algorithms in terms of network utility, delay, cost, and acceptance ratio.

Cost-Efficient Virtual Network Function Placement in an Industrial Edge System: A Proposed Method

Edge computing has been a promising framework to provide effective computing services for edge users in most industrial environments. Due to the limited resources at the edge, it is not suitable for massive requirements such as the virtual reality process, communication, and so on. Cloud computing can provide sufficient resources for those requirements, but it has some latency lags. Industrial edge clouds enable flexible service providers for industrial applications by merging edge and cloud computing. The virtual network function (VNF) is one approach, while placement is a dominant solution. However, this solution may increase the cost of communication as the nodes are more distant. In this article, we investigate the resource allocation problem for VNF placement in industrial edge systems. The aim is to minimize the overall cost of the whole placement procedure, including operating cost, communication cost, and placement cost. We formulate this VNF placement problem as an integer programming problem, and due to the complexity of the problem, we propose a heuristic method to solve it. Numerical simulation shows that our proposed method can significantly cut down the cost while meeting the multiple resource constraints.

FIPAM: Fuzzy Inference Based Placement and Migration Approach for NFV-Based IoTs

The advancement and spread of the Internet-of-Things (IoT) have massively been increased over a decade. With the widespread of IoT networks, it is becoming difficult to acquire and execute real-time data. Network function virtualization (NFV) provides a flexible and efficient solution for IoT-based applications and service management. NFV creates a virtualized environment that can run a large number of micro-services for different IoT applications by using the virtual network functions (VNFs) through placement and chaining. In this paper, we propose a novel fuzzy inference-based placement and migration (FIPAM) approach for placement and migration/chaining of VNFs to ensure that resource allocation is carefully carried out during VNF orchestration and embedding. Firstly, we formulate the VNF chaining and placement problem. Secondly, we propose a lightweight VNF placement solution that considers the underlying network conditions while making the placement decisions. A novel usage of fuzzy inference is proposed to optimize the chaining mechanism along with the dynamic instantiation of VNFs to meet specific service needs. Simulation results are shown to validate the superiority of the proposed algorithm over existing schemes.

A Novel Strategy for VNF Placement in Edge Computing Environments

Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models combined with automated management. Different management and orchestration challenges arise in such virtualized and distributed environments. A major challenge in the selection of the most suitable edge nodes is that of deploying virtual network functions (VNFs) to meet requests from multiple users. This article addresses the VNF placement problem by providing a novel integer linear programming (ILP) optimization model and a novel VNF placement algorithm. In our definition, the multi-objective optimization problem aims to (i) minimize the energy consumption in the edge nodes; (ii) minimize the total latency; and (iii) reducing the total cost of the infrastructure. Our new solution formulates the VNF placement problem by taking these three objectives into account simultaneously. In addition, the novel VNF placement algorithm leverages VNF sharing, which reuses VNF instances already placed to potentially reduce computational resource usage. Such a feature is still little explored in the community. Through simulation, numerical results show that our approach can perform better than other approaches found in the literature regarding resource consumption and the number of SFC requests met.

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.

Service Chain Placement Optimization in 5G FANET-Based Network Edge

Next generation (5G&B) networks will require the design of new architectures and technologies capable of supporting time-sensitive applications. To this aim, the implementation of service chaining as a tool to simplify and lighten the execution of complex tasks by splitting these into simpler functions that can be provided at the network edge can represent a critical improvement. Recently, it has been proposed to implement the network edge by way of unmanned aerial vehicles (UAVs) organized as a flying ad hoc network. In this article, we shed light on the new paradigm of service chaining at the network edge and present an approach that aims at solving an optimal virtual network function placement problem, taking into account UAV computing and battery limitations, as well as latency constraints, tightly associated with the specific application to be supported. Some numerical results assess the effectiveness of this methodology.

Dynamic Resource Allocation for Virtual Network Function Placement in Satellite Edge Clouds

Satellite edge computing has become a promising way to provide computing services for Internet of Things (IoT) users in remote areas, which are out of the coverage of terrestrial networks. Nevertheless, it is not suitable for large-scale IoT users due to the resource limitation of satellites. Cloud computing can provide sufficient available resources for IoT users, but it does not meet delay-sensitive services as high network latency. Satellite edge clouds can facilitate flexible service provisioning for numerous IoT users by incorporating the advantages of edge computing and cloud computing. In this paper, we investigate the dynamic resource allocation problem for virtual network function (VNF) placement in satellite edge clouds. The aim is to minimize the network bandwidth cost and the service end-to-end delay jointly. We formulate the VNF placement problem as an integer non-linear programming problem and then propose a distributed VNF placement (D-VNFP) algorithm to address it. The experiments are conducted to evaluate the performance of the proposed D-VNFP algorithm, where Viterbi and Game theory are considered as the baseline algorithms. The results show that the proposed D-VNFP algorithm is effective and efficient for solving the VNF placement problem in satellite edge clouds.

Virtual Network Function Placement in Satellite Edge Computing With a Potential Game Approach

Satellite networks, as a supplement to terrestrial networks, can provide effective computing services for Internet of Things (IoT) users in remote areas. Due to the resource limitation of satellites, such as in computing, storage, and energy, a computation task from an IoT user can be divided into several parts and cooperatively accomplished by multiple satellites to improve the overall operational efficiency of satellite networks. Network function virtualization (NFV) is viewed as a new paradigm in allocating network resources on-demand. Satellite edge computing combined with the NFV technology is becoming an emerging topic. In this paper, we propose a potential game approach for virtual network function (VNF) placement in satellite edge computing. The VNF placement problem aims to minimize the deployment cost for each user request, furthermore, we consider that a satellite network should provide computing services for as many user requests as possible. We formulate the VNF placement problem as a potential game to maximize the overall network payoff and analyze the problem by a game-theoretical approach. We implement a decentralized resource allocation algorithm based on a potential game (PGRA) to tackle the VNF placement problem by finding a Nash equilibrium. Finally, we conduct the experiments to evaluate the performance of the proposed PGRA algorithm. The simulation results show that the proposed PGRA algorithm can effectively address the VNF placement problem in satellite edge computing.

A cost-efficient, load-balanced and fragmentation-aware approach for deployment of VNF service chains in Elastic Optical Networks

By emerging Network Function Virtualization (NFV), service providers use VNFs (Virtual Network Functions) and VNF service chain (VNF-SC) as an agile, scalable, and cost-efficient way for providing new network services. As traditional backbone networks fail to deal with the high volume and burstiness of traffic patterns generated by today popular services, using Elastic Optical Networks (EONs) for interconnecting data centers (inter-DC EONs) gets more attention. EONs are an ideal backbone for supporting NFV technology and transporting VNF-SC heterogeneous traffics. In this paper, we propose a solution named CELFA for the problem of VNF-SC deployment to minimize resource costs, balance resource utilization, and avoid spectrum fragmentation in EONs. At first, to find an optimal solution for saving computing and optical resources, we formulate the VNF placement problem as an Integer Quadratic Program (IQP). Then, we propose an efficient load-balanced and fragmentation-aware algorithm for the problem of Routing and Spectrum Assignment (RSA). The performance of the proposed strategy is evaluated based on resource balancing and blocking probability metrics. The simulation results verify that the proposed algorithm outperforms existing algorithms in terms of all important metrics.

Joint Dynamical VNF Placement and SFC Routing in NFV-Enabled SDNs

Due to that Service Function Chain (SFC) permits the forwarding of flows along a predetermined sequence chain of Virtual Network Functions (VNFs), it has become a common service in Network Function Virtualization (NFV)-enabled Software Defined Networks (SDNs). Generally, since there are multiple same VNF-instances in NFV-Enabled SDNs, this brings a great challenge for selecting or placing the required VNF-instances to satisfy the routing of SFC Request flows (SRs). In this paper, we study the routing problem for SRs by jointly considering dynamical VNF placement and multiple Resources and Quality of Service (QoS) constraints in NFV-Enabled SDNs. Specifically, we first define two optimization problems: one is the Dynamical VNF Placement and Routing Problem for SRs (DVPRP) and the other is the Delay, packet Loss and Jitter Aware Dynamical VNF Placement and Routing Problem for SRs (DLJA-DVPRP). We then formulate the two problems as Integer Linear Programming (ILP) problems. Next, we creatively devise an auxiliary edge-weight graph and propose two efficient algorithms to solve the problems with the aim of minimizing the resource consumption costs as well as ensuring multiple QoS constraints. Especially, we utilize the shortest path algorithm based on Lagrange relaxation method to solve the DLJA-DVPRP with multiple QoS constraints. Compared with existing algorithms, simulation results demonstrate our proposed algorithms have better performance in terms of throughput, traffic acceptance rate and load balance.

Reliability-Aware VNF Placement Using a Probability-Based Approach

Network function virtualization (NFV) is a new network architecture concept that simplifies the deployment of network services and improves service management. However, it is challenging for a network service provider (NSP) to decide where to place virtual network functions (VNFs). Most previous studies have considered only single-chain services, wherein the VNFs for a request are executed in sequence. In contrast to previous approaches, we consider more general and practical situations in which the VNFs of a request can be executed in parallel and are represented as a forwarding graph. Our objective is to maximize the profits earned by providing network services while satisfying the delay requirements of requests. We formulate the VNF placement problem as an integer linear programming (ILP) problem. Due to the complexity of this problem, we propose a probability-based approach called PBP, in which the placements of the VNFs are determined based on their probabilities of contributing to the profit. Furthermore, we propose a heuristic reliability-aware algorithm to guarantee service reliability, in which each VNF of a request is assigned a backup that can be shared with other requests. Simulation experiments show that PBP achieves a much shorter computation time than previous algorithms while earning higher profit, and furthermore, our reliability-aware algorithm provides the same reliability as a previous algorithm while yielding much higher profit.

Resource Optimization and Delay Guarantee Virtual Network Function Placement for Mapping SFC Requests in Cloud Networks

Since the advent of network function virtualization (NFV), cloud service providers (CSPs) can implement traditional dedicated network devices as software and flexibly instantiate network functions (NFs) on common off-the-shelf servers. NFV technology enables CSPs to deploy their NFs to a cloud data center in the form of virtual network functions (VNFs) without costly capital expenditures and operating expenses. However, it is an essential but intractable issue for CSPs to devise a suitable VNF placement scheme to optimize network resource consumption and improve network performance. In this article, we focus on the VNF placement problem for mapping users’ service function chain requests (SFCRs) in cloud networks. To enhance network resource utilization, we consider the fundamental resource overheads and implementation method of VNFs. The VNF placement problem is formulated as an integer linear programming model with the aim of minimizing the total network resource consumption while guaranteeing the delay requirements of SFCRs. We devise a two-phase optimization solution (TPOS) to solve the problem. TPOS contains a mapping phase to map SFCRs on servers and an adjustment phase to optimize the placement of VNFs and VNF requests. Evaluation results demonstrate that TPOS can derive near-optimal server resource consumption and significantly enhance network resource utilization. TPOS can guarantee the delay requirements of SFCRs and outperform contrastive schemes in terms of activated servers, SFCR acceptance ratio, and average VNF utilization.

Recent Advances of Resource Allocation in Network Function Virtualization

Network Function Virtualization (NFV) has been emerging as an appealing solution that transforms complex network functions from dedicated hardware implementations to software instances running in a virtualized environment. Due to the numerous advantages such as flexibility, efficiency, scalability, short deployment cycles, and service upgrade, NFV has been widely recognized as the next-generation network service provisioning paradigm. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. These VNFs are pitched with a predefined order through which data flows traverse, and it is also known as the Service Function Chaining (SFC). In this article, we provide an overview of recent advances of resource allocation in NFV. We generalize and analyze four representative resource allocation problems, namely, (1) the VNF Placement and Traffic Routing problem, (2) VNF Placement problem, (3) Traffic Routing problem in NFV, and (4) the VNF Redeployment and Consolidation problem. After that, we study the delay calculation models and VNF protection (availability) models in NFV resource allocation, which are two important Quality of Service (QoS) parameters. Subsequently, we classify and summarize the representative work for solving the generalized problems by considering various QoS parameters (e.g., cost, delay, reliability, and energy) and different scenarios (e.g., edge cloud, online provisioning, and distributed provisioning). Finally, we conclude our article with a short discussion on the state-of-the-art and emerging topics in the related fields, and highlight areas where we expect high potential for future research.

Efficient Placement of Service Function Chains in Cloud Computing Environments

The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

Holu: Power-Aware and Delay-Constrained VNF Placement and Chaining

Service function chains (SFCs) are an ordered set of virtual network functions (VNFs) which can realize a specific network service. Enabled by virtualization technologies, these VNFs are hosted on physical machines (PMs), and interconnected by network switches. In today networks, these resources are usually under-utilized and/or over-provisioned, resulting in power-inefficient deployments. To improve power-efficiency, SFCs should be deployed utilizing the minimum number of PMs and network equipment, which are not concomitant. Considering the existing PM and switch power consumption models and their resource constraints, we formulate the power-aware and delay-constrained joint VNF placement and routing (PD-VPR) problem as an Integer Linear Program (ILP). Due to the NP-completeness of the problem, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holu</i> , a fast heuristic framework that efficiently solves the PD-VPR problem in an online manner. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holu</i> decomposes the PD-VPR into two sub-problems and solve them sequentially: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VNF placement</i> problem that consists of mapping the VNFs to PMs using a centrality-based ranking method, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">routing</i> problem that efficiently splits the delay budget between consecutive VNFs of the SFC, and finds a Delay-Constrained Least-Cost (DCLC) shortest-path through the selected PMs (hosting VNFs) using the Lagrange Relaxation based Aggregated Cost (LARAC) algorithm. Our simulation results indicate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holu</i> outperforms the state-of-the-art algorithms in terms of total power consumption and acceptance rate by 24.7% and 31%, respectively.

Service Chaining Placement Based on Satellite Mission Planning in Ground Station Networks

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this article, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer nonlinear programming (INLP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks.

Energy-Saving and Resource-Efficient Algorithm for Virtual Network Function Placement With Network Scaling

As an significant paradigm change in network service provisioning, network function virtualization (NFV) can accommodate sophisticated and heterogeneous network services by placing miscellaneous virtual network functions (VNFs) on the specific software-based middleboxes. However, with every-growing energy and construction cost, network operators still face several challenges in terms of adaptation to fluctuant network load and the reduction of energy consumption. Specifically, it is still an open problem to resource-efficiently place VNFs on substrate network while saving network's energy consumption. In this article, the problem of VNF placement is investigated for the optimization of energy consumption and resource utilization. We first present the formulations of power and resource utilization, and then we formulate the VNF placement problem as a binary integer programming (BIP) optimization problem. A novel VNF placement algorithm, named as VPANS, is designed to achieve the efficient VNF placement solution based on matching theory and the dynamical adjustment of the substrate network's scale while it is redundant or inadequate for real-time load. The performance evaluation shows that compared with other three contrast algorithms, our proposed VPANS algorithm not only achieve a maximum power gain of 34.67 percent, but also achieve a better resource utilization and revenue.

Combining Deep Reinforcement Learning With Graph Neural Networks for Optimal VNF Placement

Network Function Virtualization (NFV) technology utilizes software to implement network function as virtual instances, which reduces the cost on various middlebox hardware. A Virtual Network Function (VNF) instance requires multiple resource types in the network (e.g., CPU, memory). Therefore, an efficient VNF placement policy should consider both the resource utilization problem and the Quality of Service (QoS) of flows, which is proved NP-hard. Recent studies employ Deep Reinforcement Learning (DRL) to solve the VNF placement problem, but existing DRL-based solutions cannot generalize well to different topologies. In this letter, we propose to combine the advantage of DRL and Graph Neural Network (GNN) to design our VNF placement scheme DeepOpt. Simulation results show that DeepOpt outperforms the state-of-the-art VNF placement schemes and shows a much better generalization ability in different network topologies.