Virtual Network Requests Research Articles

Self-adjusting resilient control plane for virtual software-defined optical networks

Optical networks must promptly respond to failures and efficiently handle dynamic traffic in order to fulfill their role as a critical infrastructure. Leveraging network softwarization and virtualization, virtual software-defined networks offer sufficient flexibility towards this goal by sharing the physical infrastructure among multiple tenants whose traffic must traverse the network hypervisor. In a resilient optical control plane each switch must be assigned to a primary and backup hypervisor instance through short control paths, which challenge will be addressed in this paper. First, we propose an intelligent greedy hypervisor placement heuristic which maximizes acceptance ratio for current, and preparedness for future requests. Secondly, we introduce a graph neural network model that can be seamlessly integrated with either our integer linear program or heuristic method to yield high-quality placements in significantly less time compared to our prior solutions. This enhancement renders our approach applicable to larger networks, significantly expanding its practical utility. Finally, we propose a self-adjusting hypervisor migration strategy, which continuously adapts the placement to the dynamically changing virtual network requests, thus, ensuring service continuity by avoiding frequent control plane reconfigurations. Through simulations we show that our hypervisor placement and migration strategies provide a balanced control load while they can handle a wide variety of changes.

Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.

Energy-Efficient Virtual Network Embedding: A Deep Reinforcement Learning Approach Based on Graph Convolutional Networks

Network virtualization (NV) technology is the cornerstone of modern network architectures, offering significant advantages in resource utilization, flexibility, security, and streamlined management. By enabling the deployment of multiple virtual network requests (VNRs) within a single base network through virtual network embedding (VNE), NV technology can substantially reduce the operational costs and energy consumption. However, the existing algorithms for energy-efficient VNE have limitations, including manual tuning for heuristic routing policies, inefficient feature extraction in traditional intelligent algorithms, and a lack of consideration of periodic traffic fluctuations. To address these limitations, this paper introduces a novel approach that leverages deep reinforcement learning (DRL) to enhance the efficiency of traditional methods. We employ graph convolutional networks (GCNs) for feature extraction, capturing the nuances of network graph structures, and integrate periodic traffic fluctuations as a key constraint in our model. This allows for the predictive embedding of VNRs that is both energy-efficient and responsive to dynamic network conditions. Our research aims to develop an energy-efficient VNE algorithm that dynamically adapts to network traffic patterns, thereby optimizing resource allocation and reducing energy consumption. Extensive simulation experiments demonstrate that our proposed algorithm achieves an average reduction of 22.4% in energy consumption and 41.0% in active substrate nodes, along with a 23.4% improvement in the acceptance rate compared to other algorithms.

EFraS: Emulated framework to develop and analyze dynamic Virtual Network Embedding strategies over SDN infrastructure

The integration of Software-Defined Networking (SDN) into Network Virtualization (NV) significantly enhances network management, isolation, and troubleshooting capabilities. However, it brings forth the intricate challenge of allocating Substrate Network (SN) resources for various Virtual Network Requests (VNRs), a process known as Virtual Network Embedding (VNE). It encompasses solving two intractable sub-problems: embedding Virtual Machines (VMs) and embedding Virtual Links (VLs). While the research community has focused on formulating embedding strategies, there has been less emphasis on practical implementation at a laboratory scale, which is crucial for comprehensive design, development, testing, and validation policies for large-scale systems. However, conducting tests using commercial providers presents challenges due to the scale of the problem and associated costs. Moreover, current simulators lack accuracy in representing the complexities of communication patterns, resource allocation, and support for SDN-specific features. These limitations result in inefficient implementations and reduced adaptability, hindering seamless integration with commercial cloud providers. To address this gap, this work introduces EFraS (Emulated Framework for Dynamic VNE Strategies over SDN). The goal is to aid developers and researchers in iterating, testing, and evaluating VNE solutions seamlessly, leveraging a modular design and customized reconfigurability. EFraS offers various functionalities, including generating real-world SN topologies and VNRs. Additionally, it integrates with a diverse set of evaluation metrics to streamline the testing and validation process. EFraS leverages Mininet, Ryu controller, and OpenFlow switches to closely emulate real-time setups. Moreover, we integrate EFraS with various state-of-the-art VNE schemes, ensuring the effective validation of embedding algorithms.

Load-Balanced Virtual Network Embedding Based on Deep Reinforcement Learning for 6G Regional Satellite Networks

Regional satellite networks are capable of supporting denser coverage and more reliable communications in the target area and hence have been viewed as an essential part of the sixth generation (6G) communication system. Since satellite networks are time-varying and have limited resources, efficient resource management schemes are needed to accommodate massive and ubiquitous service requests. As a remedy, virtual network embedding (VNE) can enable diverse virtual network requests (VNRs) to share the same substrate network resources to improve resource utilization. However, existing works are few and mainly rely on heuristic methods, whose static embedding strategies cannot be optimized according to the resource state. In this paper, we propose a deep reinforcement learning (DRL) aided load-balanced VNE algorithm (DRL-LBVNE) for the regional satellite networks, where we first build a low-cost regional satellite network scenario and derive its multi-fold coverage constraints. Besides, we design a novel preprocessing scheme to reduce mapping failure, where the satellite network is divided into multiple mapping regions, and VNRs are only deployed in the mapping region with the lowest load. In the node mapping stage, the DRL agent can calculate the embedding probabilities of each physical node based on the environment state. Moreover, a comprehensive metric for path selection is presented in the link mapping stage. Simulation results show that the DRL-LBVNE algorithm outperforms the other five state-of-art algorithms in acceptance rate, resource utilization, and average delay, reflecting better adaptability to dynamic satellite networks.

Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Non-Geostationary Orbit satellite (NGSO) is an essential element in 5G Non-Terrestrial Networks (NTNs), which can operate either independently or as complementary parts to terrestrial systems to boost the network capacity, coverage and resilience. Due to the highly dynamic topologies, one of the challenges in NGSO is how to harmonize the network virtualized resources to satisfy diverse quality of service requirements in an efficient manner. In this paper, we investigate Virtual Network Embedding (VNE) for integrated NGSO-terrestrial systems while considering dynamic topologies. We propose a Mixed Binary Linear Programming (MBLP) formulation for a Dynamic Topology-Aware VNE (DTA-VNE) algorithm. Given priori information about the network’s evolution over time, DTA-VNE plans the embedding for each Virtual Network Request (VNR) over its lifetime. In a highly dynamic environment, the VNE decision can be varying for different VNRs at the expense of a considerable cost of migrating traffic and reconfiguring resources. DTA-VNE aims at minimizing this migration cost to avoid unnecessary re-mappings. To tackle the exponential complexity of the MBLP, we propose an efficient algorithm based on relaxation approaches (DTA-R) to solve large-scale problems. In numerical results, the effectiveness of the proposed DTA-R is demonstrated with much lower migration cost than the conventional implementations. The trade-off between the computation time and migration cost of DTA-R is studied. Finally, we test DTA-R and the baselines in our developed MultI-layer awaRe SDN-based testbed for SAtellite-Terrestrial networks (MIRSAT) to precisely quantify the packet lost for each migration. DTA-R proved to reduce the packet lost by ~2.5-5% compared to baselines.

Network Resource Allocation Algorithm Using Reinforcement Learning Policy-Based Network in a Smart Grid Scenario

The exponential growth in user numbers has resulted in an overwhelming surge in data that the smart grid must process. To tackle this challenge, edge computing emerges as a vital solution. However, the current heuristic resource scheduling approaches often suffer from resource fragmentation and consequently get stuck in local optimum solutions. This paper introduces a novel network resource allocation method for multi-domain virtual networks with the support of edge computing. The approach entails modeling the edge network as a multi-domain virtual network model and formulating resource constraints specific to the edge computing network. Secondly, a policy network is constructed for reinforcement learning (RL) and an optimal resource allocation strategy is obtained under the premise of ensuring resource requirements. In the experimental section, our algorithm is compared with three other algorithms. The experimental results show that the algorithm has an average increase of 5.30%, 8.85%, 15.47% and 22.67% in long-term average revenue–cost ratio, virtual network request acceptance ratio, long-term average revenue and CPU resource utilization, respectively.

Approximately Lossless Model Compression-Based Multilayer Virtual Network Embedding for Edge–Cloud Collaborative Services

Edge-cloud collaboration integrated with network virtualization is indispensable for diversified edge services. Meanwhile, the multilayer elastic optical network (ML-EON) is a promising underlying network for virtual network requests (VNRs) customized for edge-cloud collaborative services. However, the joint allocation of computing resources and high-dimensional ML-EON resources in virtual network embedding (VNE) will pose great computational complexity for online service deployment. In this paper, we propose an approximately lossless model compression mechanism to ease the computing burden of the VNE over ML-EON for edge-cloud collaborative services. An integer quadratic constraint programming (IQCP) model is established for the problem. Model compression based on Virtual Link Mapping Cost Estimation (VLMCE) is investigated to shield the variables and constraints related to ML-EON. In particular, the resource metric and topology metric are introduced into VLMCE to cope with resource contentions among virtual links in the same VNR and improve estimation accuracy. The model solving relies on Hopfield neural network (HNN) is further studied, where optimizing the compressed model is losslessly converted to minimizing the energy function of HNN. The experimental results reveal that the proposed mechanism guarantees an approximately lossless algorithm performance and a high time efficiency compared with the original IQCP model. The performances of VNR cost and blocking ratio are also promoted compared with the benchmarks.

A Multiple QoS Metrics-Aware Virtual Network Embedding Algorithm

Abstract As a key issue of network virtualisation, virtual network embedding (VNE) aims to embed multiple virtual network requests (VNRs) from different applications onto the substrate network effectively. In real networks, about 90% of traffic is generated by different quality of service (QoS) sensitive applications. However, most existing VNE algorithms do not account for the difference. Although several VNE algorithms considered the delay metric of applications, they usually provide strict delay guarantees for all VNRs, leading to a low VNR acceptance ratio. In this paper, we focus on the VNE problem involving multiple QoS metrics and propose a multiple QoS metrics-aware VNE algorithm based on reinforcement learning (RLQ-VNE). We first classify VNRs according to their different requirements for multiple QoS metrics including delay, jitter and packet loss rate, and then introduce reinforcement learning to implement differentiated VNE. Specifically, RLQ-VNE provides strict QoS guarantees for the VNRs with high-level QoS requirements and provides lower QoS guarantees for the VNRs with low-level QoS requirements, thus balancing the QoS guarantee and request acceptance ratio. Simulation results from multiple experimental scenarios show that RLQ-VNE improves the request acceptance ratio and network resource utilisation by sacrificing less QoS.

Node Essentiality Assessment and Distributed Collaborative Virtual Network Embedding in Datacenters

Network virtualization (NV) has extensive and significant applications in cloud computing and parallel and distributed systems. Virtual network embedding (VNE) is a key issue in NV, which is an effective means to advance systems' performance. While existing VNE research lacks resource allocation coordination between mappings of different virtual network requests, resulting in insufficient resource utilization and high overhead. In this paper, we propose a novel node essentiality evaluation model for data center networks (DCNs), and design an efficient distributed collaborative virtual network embedding. Firstly, we propose a node essentiality evaluation scheme based on dynamic model, which combines the characteristics of network topology and nodes to make the evaluation results more comprehensive. Secondly, we establish the two-stage node importance evaluation criteria for the deviation mean of the data center dynamic model and the variance based on the deviation mean. Furthermore, we investigate a nodal importance assessment method based on the data center dynamic model for perturbation testing. Finally, we design a distributed coordinated VNE algorithm ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CNI-VNE</b> ) which calculates the importance index of physical nodes through topology awareness. The proposed algorithm can increase the coordination between different request mappings, thereby reducing the mapping cost of physical node resources and minimizing the cost of VNE. We use the real Fat-tree DCN of 128 servers and 80 switches as testbed, and evaluate them from indicators such as average reliability, average bandwidth consumption, average energy consumption, and average mapping time. Massive simulation results in different scenarios show that our algorithm achieves the best performance on most indicators compared with the existing state-of-the-art proposals, mapping acceptance and average revenue increased by 19.4% and 21.3%, respectively, and DCN reduced bandwidth consumption by about 30%.

Resource allocation for dataflow applications in FANETs using anypath routing

Management of network resources in advanced IoT applications is a challenging topic due to their distributed nature from the Edge to the Cloud, and the heavy demand of real-time data from many sources to take action in the deployment. FANETs (Flying Ad-hoc Networks) are a clear example of heterogeneous multi-modal use cases, which require strict quality in the network communications, as well as the coordination of the computing capabilities, in order to operate correctly the final service. In this paper, we present a Virtual Network Embedding (VNE) framework designed for the allocation of dataflow applications, composed of nano-services that produce or consume data, in a wireless infrastructure, such as an airborne network. To address the problem, an anypath-based heuristic algorithm that considers the quality demand of the communication between nano-services is proposed, coined as Quality-Revenue Paired Anypath Dataflow VNE (QRPAD-VNE). We also provide a simulation environment for the evaluation of its performance according to the virtual network (VN) request load in the system. Finally, we show the suitability of a multi-parameter framework in conjunction with anypath routing in order to have better performance results that guarantee minimum quality in the wireless communications.

Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach

In this paper, we propose an efficient algorithm for the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements. The problem has been formulated as a mixed integer linear programming (MILP) formulation in the literature. We first propose a novel linear programming (LP) relaxation of the MILP formulation. We show that compared with a natural LP relaxation of the MILP formulation, the novel LP relaxation is much more compact in terms of smaller numbers of variables and constraints, and much stronger in terms of providing a better LP bound, which makes it particularly suitable to be embedded in an LP relaxation based algorithm. Then we design an efficient two-stage LP dynamic rounding-and-refinement algorithm based on this novel LP relaxation. In the first stage, the proposed algorithm uses an LP dynamic rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration; in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between the solution quality and the computational complexity. In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm.

Energy-Aware Virtual Network Migration for Internet of Things Over Fiber Wireless Broadband Access Network

The virtualized fiber wireless broadband access networks (V-FiWi) paradigm, effectively embedding heterogeneous virtual networks (VNs) originated from the service provider (SP) into shared substrate network (SN) provided by infrastructure provider (InP), plays a tremendous role in meeting the differentiated requirements between wireless frontend subnetwork and fiber backhaul subnetwork to achieve the interoperability of heterogeneous resource allocation. However, most of the existing V-FiWi integration systems mainly focused on the virtual network request (VNR) acceptance ratio, InP revenue, and substrate resource utilization, and they ignored the crucial issue called higher energy consumption cost, which was resulted from the imbalanced consumption of the substrate resource between the arrival and departure of VNR. In this article, we devote to exploring the energy-aware virtual network migration (EA-VNM) problem over the FiWi access technology, aiming to reoptimize the energy consumption while maintaining the high InP revenue and the large substrate resource utilization. In response to this issue, we first represent the service-oriented V-FiWi broadband access network architecture from the perspective of computing, storage, and network resource constraints, in which a migration model consisting of migration node and migration time is explained in detail. Then, we propose an enhanced KM-based energy-aware node migration (EKM-ENM) algorithm to economize on more bandwidth resource. More specially, the EA-VNM technology consists of network topology attributes and global network resources-based node-ranking measurement (NRM) phase, maximum weight matching-based node migration phase, and energy-aware link migration phase via Dijkstra shortest path algorithm. Finally, a rather large number of simulations are analyzed and evaluated numerically. Simulation results suggest that the proposed EKM-ENM algorithm outperforms the traditional embedding algorithms in terms of saving energy cost, decreasing time complexity, and improving VNR acceptance ratio.

Reinforcement Learning Assisted Bandwidth Aware Virtual Network Resource Allocation

Space-air-ground integration to support seamless coverage of ground, satellite, airborne, and marine communications, is likely to be a key trend in the 6G era. One of several key challenges in such space-air-ground integration networks (SAGINs) is to design efficient scheduling approaches for multi-dimension network resources. Due to the inherent heterogeneity characteristics, we demonstrate how can transform the network resource allocation problem in SAGINs into a multi-domain virtual network resource allocation problem, as well as proposing a reinforcement learning assisted bandwidth aware virtual network resource allocation algorithm (RL-BA-VNA). Specifically, RL-BA-VNA leverages reinforcement learning and uses a policy network as an agent to perform the node embedding. In order to support users’ exacting bandwidth requirements, we prefer to select virtual network requests with large bandwidth for embedding. Experiment findings show that the proposed algorithm RL-BA-VNA outperforms respectively the other three conventional virtual network resource allocation algorithms RL, DRL and BASELINE by an average of 2.06%, 4.93%, 11.07% in terms of long-term average reward, acceptance rate, and long term reward/cost.

Genetic algorithm enabled virtual multicast tree embedding in Software-Defined Networks

The recent network virtualization technology enables the multi-tenancy, where various virtual network requests can share the same physical network by decoupling network services from the underlying hardware architecture. The process of virtual node and link mapping onto a shared Substrate Network (SN) by satisfying the requested network resources (i.e., bandwidth, computing capacity, etc.) is referred to as Virtual Network Embedding (VNE), which is known as an NP-Hard problem. The problem of VNE aims to exhibit one-to-one (unicast) communication. However, the motivation of this research is to explore how to efficiently map virtual networks with one-to-many (multicast) communications, which are in the form of Virtual Multicast Trees (VMTs), onto an SN. This problem differs from the traditional VNE problem and has not been well-studied by the research community. To this end, we propose a novel algorithm, Modified Genetic Algorithm for Virtual Multicast Tree Embedding (MGA-VMTE), to embed VMTs onto a shared SN in this research. The proposed MGA-VMTE algorithm focuses on minimizing the network resource consumption (i.e., bandwidth) under end-to-end delay constraint in the SN while satisfying the computing request of virtual nodes. Our extensive simulations demonstrate that the MGA-VMTE algorithm outperforms the dynamic impact factor and traditional greedy-based virtual multicast tree embedding approaches regarding bandwidth consumption, acceptance ratio, and resource depletion ratio metrics on NSFNET, USNET, Random-60 node, and Random-120 node network topologies.

DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm

Network virtualization (NV) is considered a promising technology that may solve the problem of Internet rigidity. The resource competition of multiple virtual networks for shared substrate network resources is a challenging problem in NV called virtual network embedding (VNE). Existing approaches do not consider the differences between multi-tenant requests and adopt a single embedding method, resulting in poor performance. This paper proposes a virtual network embedding algorithm that distinguishes the network types requested by tenants. This method divides virtual network requests into ordinary requests and delay-sensitive requests according to the delay constraints, provides personalized mapping strategies for different networks, and flexibly responds to the resource requirements and quality of service (QoS) requirements of the virtual network. The simulation results show that, compared with other algorithms, the proposed algorithm improves the request acceptance ratio by about 2% to 15% and the substrate network resources are more effectively utilized.

Dynamic Reliability-Aware Virtual Network Embedding for Airborne Tactical Networks

Airborne tactical networks (ATNs) built on network virtualization (NV) can enable efficient information sharing for network-centric warfare by breaking the tight coupling between applications and network infrastructure and thus solving the network ossification problem. With dynamic changes during military missions, the application of virtualization is challenged by the changing demands on network resources when instantiating multiple virtual networks (VNs) on a shared substrate network (SN), known as virtual network embedding (VNE). However, existing dynamic VNE algorithms, mostly designed for wired networks, focus on the dynamic changes of single VN and do not consider the dynamic changes of different VNs. The proper processing of changes of different VNs is beneficial to improve the embedding profits of VNs. On these backgrounds, we investigate the dynamic wireless VNE that processes the dynamic changes of different VNs in the wireless environment of ATNs. A relationship matrix-based dynamic wireless VNE (DWVNE-RM) algorithm is proposed in order to maximize the acceptance ratio of VN requests. The dynamic changes of different VNs are classified into negative changes (requiring resource) and positive changes (releasing resource). Then, the relationship matrix between negative and positive changes is constructed to properly process the resource relationship among different changes in order to improve the acceptance ratio. In addition, the complex interference of ATNs is considered in the mapping or remapping of virtual nodes and links to improve the reliability. Through extensive simulation, DWVNE-RM is compared with multiple existing dynamic algorithms and outperforms over others.

Application of Meta-Heuristics in 5G Network Slicing: A Systematic Review of the Literature.

Network slicing is a vital component of the 5G system to support diverse network scenarios, creating virtual networks (slices) by mapping virtual network requests to real networks. The mapping is an arduous computing process, mathematically studied and known as the Virtual Network Embedding (VNE) problem, and its complexity is NP-Hard. The mapping process is oriented to respect the QoS demands from the virtual network requests and the available resources in the physical-substrate infrastructure. Meta-heuristic approaches are a suitable way to solve the VNE problems because of their capacity to escape from the local optimum and adapt the solution search to complex networks; these abilities are essential in 5G networks scenarios. This article presents a systematic review of meta-heuristics organized by application, development and problem-solving approaches to VNE. It also provides the standard parameters to model the infrastructure and virtual network requests to simulate network slicing as a service. Finally, our work proposes some future research based on the discovered gaps.

Shared Protection-Based Virtual Network Embedding Over Elastic Optical Networks

This paper proposes a survivable virtual network embedding model over elastic optical networks considering shared protection against any single substrate node or link failure. A virtual network request is embedded in the substrate network with allocating the primary and backup resources which are node-disjoint. Modulation selection and spectrum allocation with constraints from elastic optical networks are considered in the embedding procedure. We consider the backup computing and transmission resource sharing to reduce the required backup resources. We formulate the proposed model as an integer linear programming problem to minimize the rejection ratio for a given set of virtual network requests. We introduce a greedy-based approach that promotes resource sharing to handle the larger-size problem in a practical time. In order to further improve the performance on the objective value, a deep reinforcement learning-based approach with polynomial time complexity in each episode is developed to solve the problem in multiple stages. We design a dedicated learning agent for each stage considering the problem property. We analyze the usages of different approaches based on performance evaluation. The numerical results show that, compared to a conventional model that adopts dedicated protection, the proposed model with shared protection reduces the rejection ratio by 15% on average in our examined cases.

Toward Adaptive Joint Node and Link Mapping Algorithms for Embedding Virtual Networks: A Conciliation Strategy

Network virtualization (NV) has emerged as a momentous facilitator for a notable triumph of future networks by allowing a flexibility, cost-efficiency and on-demand services through the deployment of heterogeneous network service requests on a shared physical infrastructure. The most major challenge of NV is to efficiently and effectively map diversified virtual network requests (VNRs), comprising a set of virtual nodes connected by virtual links, onto a shared substrate network meeting various stringent resource constraints. Most of the research papers in this field have merely focused on separate virtual node mapping (VNoM) or virtual link mapping (VLiM) with scalable heuristic algorithms for simple implementations. Unfortunately, the lack of a coordination between node and link mapping stages might cause low embedding results. In this paper, we present a new approach relied upon Genetic Algorithm (GA), that jointly coordinates virtual node and link mappings where the link mapping is based on three different path searching methods. Moreover, a novel heuristic conciliation mechanism is proposed to deal with a set of possibly infeasible link mappings while exploring embedding solutions within the operations of GA algorithm. Extensive performance results indicate that our proposed GA-based algorithms outperform state-of-the-art virtual mapping algorithms in all evaluation metrics we adopt.