Virtual Network Functions Placement Research Articles

A hybrid model for VNF deployment capable of responding to online requests at network edge

Network function virtualization (NFV) is a new network architecture that replaces dedicated hardware appliances with software instances and run them via software virtualization on general-purpose servers at edge clouds. As edge clouds are resource-constrained, optimal placement of virtual network functions (VNFs) is a challenging task. This problem has proven to be NP-hard, and thus, metaheuristic algorithms have been widely used to solve it. However, these techniques suffer from high computational complexity due to their iterative, time-consuming process and cannot be performed in real-time for online requests. This paper proposes a hybrid model based on heuristic algorithms and the Whale Optimization Algorithm (WOA) to optimally assign VNFs to physical servers at edge clouds. In this method, a multi-criteria heuristic model is used for online VNF deployment, while WOA is performed offline to tune the hyperparameters of the heuristic model. Simulation results demonstrate the efficiency of the proposed method against existing techniques in terms of power consumption and acceptance rate.

Multi-objective Service Function Chain placement in 5G cellular networks based on meta-heuristic approach

With the emergence of new applications driven by the popularization of mobile devices, the next generation of mobile networks faces challenges to meet different requirements. Virtual Network Functions (VNFs) have been deployed to minimize operational costs and make network management more flexible. In this sense, strategies for VNF placement can impact different metrics of interest. Invoking and visiting VNFs in a specific execution order may be required for different use cases, resulting in a complete network service called Service Function Chain (SFC). The SFC placement problem is to define a feasible path in the physical infrastructure whose nodes and edges meet the computational and bandwidth requirements for the VNFs and virtual links, respectively. It has already been proved that this process is NP-hard and it is difficult to find an optimal solution to this problem. Therefore, in this paper, we propose the use of meta-heuristics to solve the SFC placement problem in cellular networks. We consider a triathlon competition leading to different mobility patterns. We collected real data about the competitors to simulate their movements through the scenario as well as the measured signal quality of the network. We formulate the SFC placement problem as a multi-objective problem where we try to minimize the placement cost and the total SFC delay. To solve the problem, we propose the use of two algorithms, NSGA-II and GDE3, which compare two different greedy approaches that prioritize the different optimization metrics considered in this work. Our results show that the meta-heuristics provide better results for each of the metrics. For all competition stages, GDE3 presented a slightly lower placement costs than NSGA-II, while NSGA-II had a lower delay in some scenarios.

An accelerated Benders decomposition approach for virtual base station formation in stochastic Cloud-RANs

In Cloud-RANs, the baseband unit (BBU) is separated from the remote radio head (RRH) at the cell site and migrated to a server cloud to decrease deployment and maintenance costs. A base station (BS) can be formed as a network slice in which the virtual network functions (VNFs) of its BBU can be placed on any server by network virtualization (NFV) technology and logically connected by software-defined networking (SDN). However, the uncertain demands of users make it very difficult to determine a network slice with appropriate VNF placement and flow routing between VNFs. We aim to minimize the cost of providing BS services while meeting their time-varying demands. We consider the network slice scheme and fronthaul establishment jointly and formulate this as an integer linear programming (ILP) problem. We propose scenario classification Benders decomposition (CBD) based on Benders decomposition (BD) but with fewer Benders cuts, accelerating the solution convergence. Moreover, we propose an efficient heuristic approach to obtain good solutions for the ILP problem based on the solutions of its relaxed problems obtained by BD or CBD. The numerical experiments show that when combining our heuristic approach with CBD, the results are close to the optimal solutions of the ILP problem, while the computation time is significantly shorter than those of previous approaches.

Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm

Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods.

Multi-source multicast service chaining model guaranteeing reliability of network services

Service chaining provides network services to users by processing packets with a series of virtualized network functions (VNFs). Multicast communication can suppress the network load compared to unicast communication when the same service is provided to multiple users simultaneously. Network providers need to ensure that services are provided to users under reliability requirements. Existing service chaining models can cause failure-intolerant or cost-inefficient resource allocation for multicast services. This paper proposes a multi-source multicast service chaining model that guarantees the reliability of network services with flexible routing and VNF placement. The proposed model determines the routing and VNF placement for each service chain request with redundancy so as to minimize the total resource utilization cost while satisfying the reliability requirement. In order to obtain cost-efficient feasible solutions, we introduce two heuristic algorithms. One is an algorithm that iteratively solves an integer linear programming problem until the reliability requirements are satisfied for all requests. The other is an algorithm that determines the VNF placement by using a metric based on the betweenness centrality and server failure rate. Numerical results show that the proposed model allocates the network and computation resources for services with lower total cost compared to the existing model. We also show illustrative examples of the resource allocation to compare the characteristics of two heuristic algorithms.

Subswarm-guided ant colony optimization with enhanced pheromone update mechanism and beam search for VNF placement and routing

Network Function Virtualization (NFV) is a novel technology that enables flexible and cost-effective networks by replacing the hardware-based middleboxes with software run on virtual machines called virtual network function (VNF). In NFV, each Service Function Chain (SFC) is required to be a set of ordered VNF from that must be optimally located across servers/distributed data centers. This paper studies the multi-objective virtual network function placement and routing problem (MO-VNFPR), which involves placing VNFs optimally in servers nodes and assigning server resources efficiently to these VNFs to satisfy user’s requests in the networks. Most existing methods for this problem only handled one requests or considered VNF placement and SFC routing separately. In this paper, we consider MO-VNFPR with two objectives: (i) minimize the overall cost by deploying servers and installing VNFs; (ii) reduce the network delay. A metaheuristic called subswarm-guided ant colony optimization (named SgACO) is introduced to efficiently solve MO-VNFPR. In SgACO, a subswarm-guided mechanism is adopted to satisfy requests at once by reorganizing the ant colony into subswarms. Moreover, an enhanced pheromone update mechanism and beam search are developed to investigate the search space efficiently. Experimental simulations on COGENT, CONUS, and NSF network topologies demonstrate that our algorithm achieves competitive results and performs better against other existing algorithms.

Energy-Efficient Placement of Virtual Network Functions in a Wireless Mesh Network

Energy-Efficient Placement of Virtual Network Functions in a Wireless Mesh Network

VNF Placement and Dynamic NUMA Node Selection Through Core Consolidation at the Edge and Cloud

VNF Placement and Dynamic NUMA Node Selection Through Core Consolidation at the Edge and Cloud

VNF and CNF Placement in 5G: Recent Advances and Future Trends

With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network’s performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include micro-services, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

Virtual Network Function Placement for Serving Weighted Services in NFV-Enabled Networks

Network function virtualization (NFV) is a novel network architecture yielding many optimization choices to Internet service providers due to its flexibility. Based on this technique, a network service is constructed from a chain of virtual network functions (VNFs). Although many studies deeply investigate the NFV and propose approaches to address the problem of deploying services in physical networks under different perspectives, they do not consider the importance of services. This issue becomes more critical once the number of network services ever-increasing while the network resources are limited. Here, each service is assigned a weight representing its importance. From this aspect, maximizing the total weight of services accepted to deploy over the physical network is a critical problem and poses many challenges, particularly for networks with limited resources. Therefore, we call this issue the VNF placement for weighted services (VPWS) problem. We then propose three algorithms termed first-fit server algorithm (FSA), best-fit server algorithm (BSA), and service segment-based algorithm (SSBA), respectively, to address the VPWS problem. Eventually, we perform simulations to evaluate the proposed algorithms and factors that affect the VNF placement. The experiment indicates that the total weight yielded by the SSBA outperforms those of the FSA and BSA.

HyScaler: A Dynamic, Hybrid VNF Scaling System for Building Elastic Service Function Chains Across Multiple Servers

Dynamically scaling Virtual Network Function (VNF) is essential for network operators to make their networks accommodate time-varying network traffic. However, current research only applies homogeneous VNF scaling or focuses on mathematical optimizations without implementations in a real-world environment. To build this gap, this paper presents HyScaler, a dynamic, hybrid VNF scaling system implemented in an open-source NFV platform. With HyScaler, we can build elastic Service Function Chains (SFCs) by scaling overloaded VNF Instances (VNFIs) across multiple CPU cores or physical servers. HyScaler implements this by (1) monitoring traffic loads for each deployed VNFI; (2) scaling overloaded VNFIs through all kinds of VNF scaling methods; and (3) placing and chaining the new VNFI using the Global VNF Placement and Chaining (GVPC) algorithm. Extensive experiments are conducted to validate the scalability of our HyScaler and the effectiveness of the GVPC algorithm. Compared with the original NFV platform, experimental results show that HyScaler can improve the performance of VNFIs by about 1.02 times.

Availability‐aware virtual network function placement based on multidimensional universal generating functions

AbstractNetwork function virtualization (NFV) implements network functions as software, which enables flexible, resource‐efficient, and rapid provision of network services. In NFV, network services are realized by the service function chain (SFC), which is a chain of virtual network functions (VNFs) linked in the proper order. Both availability and deployment cost are key concerns for network operators providing network services as SFC. This paper proposes a flexible VNF placement model on a per‐VNF instance basis that minimizes deployment costs while satisfying availability requirements that may be placed on SFC. This paper uses a multidimensional universal generating function (MUGF) method, which is a multistate system analysis method, to compute the availability of a multistate SFC system with multiple VNFs coexisting on a server. The MUGF method calculates the performance of the entire SFC by combining the performance of servers as determined by applying a continuous‐time Markov chain. To reduce the time to compute the SFC availability, we introduce operators to be applied to MUGF and develop an availability computing method. In addition, a heuristic algorithm for determining VNF placement targeting the lowest deployment cost possible while meeting availability requirements is presented. Numerical results show that the proposed model obtains VNF placement with lower cost than the conventional model in all examined cases. The proposed model achieves VNF placement at 58.5%–75.0% of the deployment cost of the conventional model for the same SFC availability requirements.

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.

Dynamic service function chain placement with instance reuse in Fog–Cloud​ Computing

The advent of Network Function Virtualization (NFV) technology has brought flexible provisioning to Fog–Cloud Computing-based Networks (FCCNs) for enterprises to outsource their network functions to data center networks. Service Function Chaining (SFC) is a networking concept in NFV by which traffic is steered through an ordered set of Virtual Network Functions (VNFs) composing an end-to-end service. When hundreds of users outsource their network functions to FCCN, the optimal placement of VNFs in the network becomes important for assembling SFCs with the aim of resource utilization efficiency. Motivated by the scalability shortcomings of existing schemes, we propose Deep Reinforcement Learning (DRL)-based approaches by simultaneously considering parallelized SFC and reuse of VNFs to solve this problem, i.e., Asynchronous Advantage Actor–Critic (A3C). A parallelized SFC consists of several sub-SFCs, which can reduce delay and guarantee availability. Also, reuse of preliminary VNFs in SFC placement can improve computation acceleration. The proposed scheme pursues the maximization of the long-term cumulative reward for the trade-off between Quality of Service (QoS) and service cost. The results of the experiments show that the proposed scheme performs better than the state-of-the-art methods.

A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the destination application in multiaccess edge computing (MEC) for processing. Since there are usually multiple identical VNF instances in the network and the network environment of IoT changes dynamically, placing the SFC for the IoT request flow is a significant challenge. This paper decomposes the dynamic SFC placement problem of the IoT-MEC network into two subproblems: VNF placement and path determination of routing. We first formulate these two subproblems as Markov decision processes. We then propose a meta reinforcement learning and fuzzy logic-based dynamic SFC placement approach (MRLF-SFCP). The MRLF-SFCP contains an inner model that focuses on making SFC placement decisions and an outer model that focuses on learning the initial parameters considering the dynamic IoT-MEC environment. Specifically, the approach uses fuzzy logic to pre-evaluate the link status information of the network by jointly considering available bandwidth, delay, and packet loss rate, which is helpful for model training and convergence. In comparison to existing algorithms, simulation results demonstrate that the MRLF-SFCP algorithm exhibits superior performance in terms of traffic acceptance rate, throughput, and the average reward.

Availability Model for Data Center Networks With Dynamic Migration and Multiple Traffic Flows

The evaluation of application availability is a crucial step in ensuring functionality of the data center network (DCN). The application of data center network is no more a chain of physical devices, but a chain of virtualized network function (VNF) dynamically installed in servers, i.e., the service function chain (SFC), due to the adaptation of network function virtualization (NFV). When dynamic migration is considered, the application availability evaluation would encounter problems since the state of VNF components is not independent. And in the application deployed in DCN, there is multiple traffic flow because of the serialization of 1+1 (active-standby) and N-way (load-sharing) redundant VNF, however only single flow is considered in current availability model. When dynamic migration and multiple traffic flows are considered, the application availability evaluation would encounter the following problems: 1) The application behavior is not described in the existing availability model; 2) The availability model could not quantify the influence of different traffic of each flow. This paper proposes an availability model, which consist of network evolution model (NEM) of DCN and the availability evaluation algorithm based on NEM (AEA-NEM), to evaluate the availability of different applications deployed on DCN. The NEM of DCN describes the dynamical process of migration and switch of three kinds of VNF (normal, 1+1 and N-way) in application, and the AEA-NEM evaluate the availability of application with multiple traffic flows. The simulation results show that, the steady state availability of the proposed model is consistent with it of generalized stochastic Petri net (GSPN) model. And the model could evaluate the application availability with dynamic migration and multiple traffic flows in DCN. Based on the current VNF placement and redundant strategy, the most available designing scheme is analyzed.

Slice-Based Service Function Chain Embedding for End-to-End Network Slice Deployment

This paper investigates the slice-based service function chain embedding (SBSFCE) problem, which is to embed the service function chains (SFCs) of flows from different slices on a physical network for end-to-end network slice deployment. Compared with regarding slice deployment as complete virtual network embedding (VNE), deploying slices from the perspective of SBSFCE is beneficial for achieving more delicate resource allocation and jointly optimizing virtual network function (VNF) mapping and link mapping without the need for particular virtual topology designs. However, performing effective SBSFCE also faces several key challenges like diversified and differentiated requirements of flows, inter-slice and intra-slice VNF sharing, priority-aware admission control, and VNF placement restrictions, and few existing SBSFCE works have comprehensively considered or solved these challenges. In view of this, we address the SBSFCE problem by jointly considering the above key challenges in this paper. Specifically, we formulate the SBSFCE problem as an integer linear programming (ILP) that aims to maximize flow acceptance ratios and minimize network resource costs. Then, we propose two novel heuristic algorithms, weight-oriented embedding (WOE) and weight-oriented ratio embedding (WORE), to solve the problem. Simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve near-optimal performance.

Low-Latency and Reliable Virtual Network Function Placement in Edge Clouds

Low-Latency and Reliable Virtual Network Function Placement in Edge Clouds

On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-Agent Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) and network function virtualization (NFV) are promising technologies to support emerging IoT applications, especially those computation-intensive. In NFV-enabled MEC environment, service function chain (SFC), i.e., a set of ordered virtual network functions (VNFs), can be mapped on MEC servers. Mobile devices (MDs) can offload computation-intensive applications, which can be represented by SFCs, fully or partially to MEC servers for remote execution. This paper studies the partial offloading and SFC mapping joint optimization (POSMJO) problem in an NFV-enabled MEC system, where the data from an incoming task is partitioned into two parts, with one part executed locally and the other offloaded to the edge infrastructure for execution. These two parts are independent of each other, but both need to be processed by the same SFC. The objective is to minimize the average cost in the long term which is a combination of execution delay, MD's energy consumption, and usage charge for edge computing. This problem consists of two closely related decision-making steps, namely task partition and VNF placement, which is highly complex and quite challenging. To address this, we propose a cooperative dual-agent deep reinforcement learning (CDADRL) algorithm, where two agents interact with each other. Simulation results show that the proposed algorithm outperforms three combinations of deep reinforcement learning algorithms with respect to cumulative reward and it overweighs a number of baseline algorithms in terms of execution delay, energy consumption, and usage charge.

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.