Virtual Network Function Migration Research Articles

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

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

Cost-Efficient Cluster Migration of VNFs for Service Function Chain Embedding

Network Function Virtualization (NFV) is a network architecture that separates network functions from dedicated hardware, implementing them as software modules known as Virtual Network Functions (VNFs), which are executed in virtual machines or containers. NFV increases the deployment flexibility and agility within operator networks and reduces the operating and capital expenditures significantly. In NFV, migration of VNFs can significantly reduce the embedding cost. However, stringent latency requirements between VNFs can make them tightly coupled, thus hindering each VNF from being migrated individually, and resulting in poor performance. One of the main challenges in an NFV environment is therefore to migrate a cluster of VNFs to minimize the embedding cost. In this paper, we aim to solve the problem of cluster VNF migration by considering the given inter-VNF latency requirements. We formulate the VNF migration problem as an Integer Linear Programming (ILP) and present two scalable and efficient algorithms for migrating a cluster of VNFs. Through extensive experiments, we show that our proposed algorithms are highly effective. They reduce the total embedding cost by 14% compared to the existing heuristics, while being much more scalable in terms of execution time compared to the brute-force approach.

Efficient service reconfiguration with partial virtual network function migration

Network Function Virtualization (NFV) decouples network functions from dedicated hardware devices into Virtual Network Functions (VNFs). These VNFs are chained in order as a Service Function Chain (SFC) to provision flexible and efficient services. When service requests dynamically increase, the intensive workloads often lead to node overloads and further impact the Quality of Service (QoS). Existing works address this problem by migrating VNFs from overload nodes to other low-load nodes, known as VNF migration. However, when a VNF is shared by multiple SFCs, migrating the VNF will change the mapping relationships between these SFCs and the physical network (nodes and links). That may make some SFCs traverse more links and increase their propagation latency. That violates the demand of users for low-latency services. In this paper, to minimize the impact of VNF migration on SFC latency, we propose partial VNF migration. It migrates only partial VNFs within these SFCs to minimize the overall SFC latency while reducing migration costs. As such, we leverage partial VNF migration for efficient latency minimization with the formulation of an integer linear programming (ILP) model. Given the NP-hard nature of the problem, we propose a dynamic latency-aware partial VNF migration algorithm to reduce node overloads and minimize SFC latency. Evaluation indicates that the proposed approach has 12.7%–21.8% lower average SFC latency and 12.5%–48.5% less migration cost than state-of-the-art VNF migration algorithms. And it demonstrates about 90% shorter execution time with similar minimization performance, compared to other SFC reconfiguration algorithms.

Function traffic-aware VNF migration for service restoration in an NFV-enabled IoT system

Maintaining a service continuum is crucial for providing a reliable service in an Internet-of-Things (IoT) system based on Network Function Virtualization (NFV) due to the distribution of Virtual Network Functions (VNF). We address the optimization problem of VNF migration in an NFV-enabled IoT system (NIoT), considering the dynamics of traffic volume processed by an individual IoT function. We develop an optimization model based on integer linear programming and a function traffic-aware heuristic algorithm to solve the problem. The evaluation results show that our proposed heuristic algorithm offers an approximation solution very close to the optimal VNF migration in significantly reduced time. It outperforms a benchmark solution based on Simulated Annealing in both the service restoration cost and computation time. Furthermore, our findings provide valuable insights into an investment strategy for an IoT service provider seeking to enhance the system performance in the presence of a failure.

VNF Migration in Digital Twin Network for NFV Environment

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtual Network Functions (VNFs), adapting services to the complex and dynamic network environment to enhance network performance. However, VNF migration and energy consumption pose significant challenges due to the dynamic nature of the physical network. In order to maximize the acceptance rate of Service Function Chain Requests (SFCR), and reduce VNF migration and energy consumption as much as possible, we summarize several related factors such as the node hosting state, link hosting state, energy consumption, migrated nodes, and whether the mapping is successful. We define the Markov decision process by considering the factors mentioned above. Next, we design the VNF migration algorithm utilizing actor–critic models, graph convolution networks, and LSTM networks. In order to reduce the risk of trial and error during training and prediction in deep reinforcement learning scenarios, we designed a network architecture based on a digital twin (DT). In simulation experiments, compared with the FF algorithm that greedily selects the first available node, our AC_GCN algorithm significantly improves the acceptance rate of SFC requests by 2.9 times more than the FF algorithm in small topology experiments, and 27 times more than the FF algorithm in large topology experiments. Compared with the deep reinforcement learning (DRL) algorithm, which does not consider all the above factors together, for the small topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of request acceptance rate by 13%, underperforms compared to the DRL algorithm in terms of energy consumption by 3.8%, and underperforms compared to the DRL algorithm in terms of the number of migrated nodes for 22%; for the large topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of the request acceptance rate by 7.7%, outperforms the DRL algorithm in terms of energy consumption by 0.4%, and outperforms the DRL algorithm in terms of the number of migrated nodes by 1.6%.

Virtual Network Function Migration Considering Load Balance and SFC Delay in 6G Mobile Edge Computing Networks

With the emergence of Network Function Virtualization (NFV) and Software-Defined Networks (SDN), Service Function Chaining (SFC) has evolved into a popular paradigm for carrying and fulfilling network services. However, the implementation of Mobile Edge Computing (MEC) in sixth-generation (6G) mobile networks requires efficient resource allocation mechanisms to migrate virtual network functions (VNFs). Deep learning is a promising approach to address this problem. Currently, research on VNF migration mainly focuses on how to migrate a single VNF while ignoring the VNF sharing and concurrent migration. Moreover, most existing VNF migration algorithms are complex, unscalable, and time-inefficient. This paper assumes that each placed VNF can serve multiple SFCs. We focus on selecting the best migration location for concurrently migrating VNF instances based on actual network conditions. First, we formulate the VNF migration problem as an optimization model whose goal is to minimize the end-to-end delay of all influenced SFCs while guaranteeing network load balance after migration. Next, we design a Deep Learning-based Two-Stage Algorithm (DLTSA) to solve the VNF migration problem. Finally, we combine previous experimental data to generate realistic VNF traffic patterns and evaluate the algorithm. Simulation results show that the SFC delay after migration calculated by DLTSA is close to the optimal results and much lower than the benchmarks. In addition, it effectively guarantees the load balancing of the network after migration.

AI-Based Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on users’ mobility and their requested services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act as nodes of a network function virtualization (NFV) enabled network. Our objective is to maximize energy efficiency and minimize the average delay on all services by allocating the limited radio and NFV resources. In addition, due to the traffic conditions and mobility of users, we let some virtual network functions (VNFs) migrate from their current locations to other locations to satisfy the Quality of Service requirements. We formulate our problem to find near-optimal locations of UAVs, transmit power, subcarrier assignment, VNF placement, and VNF scheduling over the UAVs and perform suitable VNF migration. Then we propose a novel hierarchical hybrid continuous and discrete action (HHCDA) deep reinforcement learning method to solve the proposed problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance is analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the average end-to-end delay by 31.5% and increases the energy efficiency by 40% compared to the state-of-the-art Deep Deterministic Policy Gradient method.

Priority-awareness VNF migration method based on deep reinforcement learning

By decoupling the software function on hardware devices, Network Function Virtualization (NFV) provides a new service architecture named Service Function Chain (SFC), which combines multiple Virtual Network Functions (VNFs) in a specific order. In order to improve the reliability and Quality of Service (QoS) of network services, VNF migration provides an effective solution for this requirement. However, traditional migration methods lack an appropriate VNF selection mechanism and consider a single network state. Moreover, how to determine the accurate migration location dynamically with machine learning is challenging. This paper proposed a novel PAVM algorithm for reliable and optimal VNF migration, which distinguishes two kinds of VNF migration schemes for network congestion and node failure states respectively, and a node and VNF priority-awareness mechanism is designed, which can select appropriate VNF on suitable node to migrate. Moreover, PAVM utilizes deep reinforcement learning algorithm to choose target node location of VNF migration, which jointly utilizes delay, load balancing of network as feedback factors to optimize the QoS. The experimental results indicate that compared with other three benchmark algorithms, PAVM can effectively reduce the transmission delay and improve node and link load balancing after the VNF migration.

Online Adaptive Interference-Aware VNF Deployment and Migration for 5G Network Slice

Based on network function virtualization (NFV) and software defined network (SDN), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network slicing</i> is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the real-time network variations and diverse performance requirements among different 5G scenarios, online adaptive VNF deployment and migration are needed to dynamically accommodate to service-specific requirements. In this paper, we first propose a time-slot based 5G network slice model, which jointly includes both edge cloud servers and core cloud servers. Since VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantify the VNF interference. To achieve our objective—maximizing the total reward of accepted requests (i.e., the total throughput minus the weighted total VNF migration cost), we propose an Online Lazy-migration Adaptive Interference-aware Algorithm (OLAIA) for real-time VNF deployment and cost-efficient VNF migration in a 5G network slice, where an Adaptive Interference-aware Algorithm (AIA) is proposed as OLAIA’s core function for placing a given set of requests’ VNFs with maximized total throughput. Through trace-driven evaluations on two typical 5G network slices, we demonstrate that OLAIA can efficiently handle the real-time network variations and the VNF interference when deploying VNFs for real-time arriving requests. In particular, OLAIA improves the total reward by 22.18% in the autonomous driving scenario and by 51.10% in the 4K/8K HD video scenario, as compared with other state-of-the-art solutions.

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

In the network function virtualization environment, dynamic changes in network traffic will lead to the dynamic changes of service function chain resource demand, which entails timely dynamic adjustment of service function chain resource configuration. At present, most researches solve this problem through virtual network function migration and link rerouting, and there exist some problems such as long service interruption time, excessive network operation cost and high penalty. This paper proposes a dynamic adjustment method of service function chain resource configuration for the dynamic changes of network traffic. First, a dynamic adjustment request of service function chain is generated according to the prediction of network traffic. Second, a dynamic adjustment strategy of service function chain resource configuration is determined according to substrate network resources. Finally, the resource configuration of a service function chain is pre-adjusted according to the dynamic adjustment strategy. Virtual network functions combination and virtual machine reusing are fully considered in this process. The experimental results show that this method can reduce the influence of service function chain resource configuration dynamic adjustment on quality of service, reduce network operation cost and improve the revenue of service providers.

Joint Resource Optimization and Delay-Aware Virtual Network Function Migration in Data Center Networks

Network Function Virtualization (NFV) is a promising paradigm that separates network functions from proprietary devices. Network service in NFV-enabled networks is achieved as a Service Function Chain (SFC), consisting of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within dynamic networks is a key challenge. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF Instance (VNFI). In this paper, we assume that each deployed VNFI is used by multiple SFCs and deal with the optimal location allocation for the concurrent migration of VNFIs based on the actual network situation. We first formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the end-to-end delay for all affected services and to guarantee network load balancing after the migration simultaneously. To this end, we prove the NP-hardness of this problem and propose the Improved Hybrid Genetic Evolution (IHGE) algorithm to address it. Besides, to reduce the computation overhead of IHGE for large-scale networks, a multi-stage heuristic algorithm based on optimal order (MSH-OR) is designed. Finally, we perform a side-by-side comparison with prior algorithms. Extensive evaluation shows that the proposed approaches can effectively reduce the average delay for different scale networks while ensuring network load balancing.

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.

High Availability Deployment of Virtual Network Function Forwarding Graph in Cloud Computing Environments

Network Function Virtualization (NFV) stands out quickly as a promising innovation in telecommunication networks. It leverages the concept of cloud technology and virtualization techniques. However, the continuity of cloud network services has become an essential availability requirement for NFV. Failure of Virtual Network Functions (VNFs) may cause critical quality assurance problems for such network services. VNF chaining and placement can be considered as the VNF Forwarding Graph (VNF-FG) mapped on a cloud provider infrastructure, also called NFV Infrastructure (NFVI). This mapping is addressed while neglecting the massive link utilization and bandwidth consumption that can be encountered during VNF recovery phase. In this paper, an Integer Linear Programming (ILP) approach is developed to model VNF-FG deployment problem while guaranteeing high availability against node/VM failures. Then, the Redundant VNF-FG Deployment (RVNF-FGD) heuristic algorithm is proposed that takes VNF migration into consideration. RVNF-FGD algorithm attempts to find a near-optimal solution that achieves a trade-off between availability and scalability with a reasonable convergence time. Thus, facilitating the practical deployment of the proposed approach. Simulation results confirm that RVNF-FGD algorithm is capable of simultaneously reducing link utilization and bandwidth consumption across the core layer of the cloud network. In addition, it reduces VNF-FG communication cost and overall energy consumption. The convergence time of RVNF-FGD algorithm is assessed by applying it to broader cloud network architectures. This assessment indicates the viability of the proposed approach in responding quickly to failures.

Online Service Function Chain Placement for Cost-Effectiveness and Network Congestion Control

The emerging network function virtualization is migrating traditional middleboxes, e.g., firewalls, load balancers, proxies, from dedicated hardware to virtual network functions (VNFs) running on commercial servers defined as network points of presence (N-PoPs). VNFs further chain up for more complex network services called service function chains (SFCs). SFCs introduce new flexibility and scalability which greatly reduce expenses and rolling out time of network services. However, chasing the lowest cost may lead to congestion on popular N-PoPs and links, thus resulting in performance degradation or violation of service-level agreements. To address this problem, we propose a novel scheme that reduces the operating cost and controls network congestion at the same time. It does so by placing VNFs and routing flows among them jointly. Given the problem is NP-hard, we design an approximation algorithm named candidate path selection (CPS) with a theoretical performance guarantee. We then consider cases when SFC demands fluctuate frequently. We propose an online candidate path selection (OCPS) algorithm to handle such cases considering the VNF migration cost. OCPS is designed to preserve good performance under various migration costs and prediction errors. Extensive simulation results highlight that CPS and OCPS algorithms perform better than baselines and comparably to the optimal solution.

Dynamic Resource Scaling for VNF Over Nonstationary Traffic: A Learning Approach

Software defined networking (SDN) and network function virtualization (NFV) are key enablers for service-level customized network slicing in fifth generation (5G) core networks. Network slices are required to be isolated from each other in terms of service performance with traffic load fluctuations. In this article, the virtual network function (VNF) scalability issue is studied to meet the quality-of-service (QoS) requirement in the presence of nonstationary traffic, through joint VNF migration and resource scaling. A traffic parameter learning method based on change point detection and Gaussian process regression (GPR) is proposed, to learn traffic parameters in a fractional Brownian motion (fBm) traffic model for each stationary traffic segment within a nonstationary traffic trace. Then, the time-varying VNF resource demand is predicted from the learned traffic parameters based on an fBm resource provisioning model. With the detected change points and predicted resource demands, a VNF migration problem is formulated as a Markov decision process (MDP) with variable-length decision epochs, to maximize the long-term reward integrating load balancing, migration cost, and resource overloading penalty. A penalty-aware deep Q-learning algorithm is proposed to incorporate awareness of resource overloading penalty, with improved performance over benchmarks in terms of training loss reduction and cumulative reward maximization.

Virtual Network Functions Migration Cost: from Identification to Prediction

The advent of the function virtualization concept, especially that of network functions, leads to important benefits for future networks. Although the orchestration of virtualized functions presents gains for network operators and clients alike, the overhead for moving functions has not been thoroughly explored so far, especially considering functions virtualized by using container technologies. In this work, we investigate orchestration costs associated with the migration of containerized virtual functions. To this end, we first perform a systematic literature review on state-of-the-art virtual function migration costs, electing time and data transferred as so. We then use a well-known container platform (LXD) to perform several orchestration experiments in a controlled environment. By analyzing the container migration process in smaller complementary steps, and designing experiments to evaluate them individually, a pattern for migration costs is observed. Linear regression is then used to derive a prediction model for the necessary time and data transferring for performing a container migration. To assess the predictor’s accuracy, we present a cloud computing use case where the predictor is deployed. Results indicate that predictions can be accurate within reasonable range, and therefore orchestration algorithms may be improved by accounting for similar prediction models when determining the migration of one or more virtualized functions.

Design and Implementation of Network-Aware VNF Migration Mechanism

By separating network functionalities from the dedicated hardware and implementing them in the form of Virtual Network Function (VNF), Network Function Virtualization (NFV) provides a new service paradigm called Service Function Chain (SFC). It is unrealistic to replace the running VNFs due to the stateful information accumulated, which makes the current VNF re-deployment or replacement approaches no longer effective enough. Such a situation is even worse when VNFs are shared. Instead of replacing VNFs, this paper proposes a VNF migration approach to maintain the network balance and performance by migrating VNFs from poor-state nodes to good-state nodes. In particular, two situations are considered, which are node-aware and link-aware VNF migrations respectively. Via migrating the VNFs that are related to the nodes or links causing Quality of Service (QoS) issues, the network and service performance can be optimized. In addition, the migration impact is also minimized by perceiving the global network states. The experimental results indicate that the service performance is improved and the load balance is achieved after the VNF migration.

A multi-criteria decision approach for minimizing the influence of VNF migration

Network Function Virtualization (NFV) has been proposed as an important paradigm to speed up the service deployment and provision, by decoupling network functionalities from the underlying hardware. These network functionalities are implemented as software that refers to as Virtual Network Function (VNF) and managed under the software defined networking environment. Thus, VNFs are critical elements for service provision. However, in order to achieve a better service deployment and provision, VNF migration is a key challenge to be addressed. On one hand, previous researches resolving the VNF migration problem are under the assumption that each VNF is independently used by one Service Function Chain (SFC) which is actually composed of a sequential set of VNFs with traffic traversing these VNFs in order. In this way, they only need to consider one SFC when fulfilling the VNF migration. In contrast, we assume that each deployed VNF may be used by different services. Thus, when migrating one VNF, we take all the services traversing this VNF into consideration, which is more practical and meaningful. On the other hand, the VNF migration is primarily transferring the internal stateful information of VNFs, which is fast. Therefore, instead of focusing on the migration process, we prefer more to minimize the influence on services after executing the VNF migration. In this paper, we first formulate the VNF migration problem as a mathematical model with the objective of minimizing the influences. Then, a multi-criteria approach is proposed to address this problem, since we need to consider multiple services simultaneously. In particular, the service priority is also considered in the proposed approach in order to meet the practical situations. Finally, the simulation results indicate that the proposed approach is effective and efficient.

Mobile-aware service function chain migration in cloud–fog computing

Network Function Virtualization (NFV) provides a good paradigm for sharing the resources of the physical network. The deployment problem of Service Function Chains (SFCs) composed of a specific order of Virtual Network Functions (VNFs) has become the focus of research. Moreover, to solve the facing challenges of the centralized cloud computing, the researchers have proposed the distributed fog computing. When the mobile user moves among different fog-based radio access networks, the SFC must be migrated. Therefore, in the paper, we research the problem of SFCs migration/remapping caused by the user movement in cloud–fog computing environments. We firstly model the migration problem of SFCs as an integer linear program; then we propose two SFC migration strategies: the minimum number of VNFs migration strategy and the two-step migration strategy, to reduce the reconfiguration cost, the migration time and downtime of SFCs and improve the remapping success ratio of SFCs; and we have designed a two-step migration algorithm to migrate SFCs. We use the cloud–fog computing environment to evaluate our proposed algorithms. The reconfiguration cost, the remapping success ratio, the migration time and the downtime of our proposed algorithms are more excellent than that of benchmark algorithm.

Virtual Network Function Migration Based on Dynamic Resource Requirements Prediction

Network function virtualization (NFV) enables flexible deployment of virtual network function (VNF) in 5G mobile communication network. Due to the inherent dynamics of network flows, fluctuated resources are required to embedding VNFs. VNF migration has become a critical issue because of the time-varying resource requirements. In this paper, we propose a real-time VNF migration algorithm based on the deep belief network (DBN) to predict future resource requirements, which resolves the problem of lacking effective prediction in the existing methods. Firstly, we propose optimizing bandwidth utilization and migration overhead simultaneously in VNF migration. Then, to model the resource utilization that evolves over time, we adopt online learning with the assistant of offline training in the prediction mechanism, and further introduce multi-task learning (MTL) in our deep architecture in order to improve the prediction accuracy. Moreover, we utilize adaptive learning rate to speed up the convergence speed of DBN. For the migration, we design a topology-aware greedy algorithm with the goal to optimize system cost by taking full advantage of the prediction result. In addition, based on tabu search, the proposed migration mechanism is further optimized. Simulation results show that the proposed scheme can achieve a good performance in reducing system cost and improving the service level agreements (SLA) of service.