Virtual Network Embedding Algorithm Research Articles

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.

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.

Performance Improvement of a Virtual Network Embedding Algorithm based on Temporal-difference Learning by Resource-Constraint-Aware Candidate Solution Selection

Performance Improvement of a Virtual Network Embedding Algorithm based on Temporal-difference Learning by Resource-Constraint-Aware Candidate Solution Selection

Heuristic energy-saving virtual network embedding algorithm based on Katz centrality

Heuristic energy-saving virtual network embedding algorithm based on Katz centrality

DVNE-DRL: dynamic virtual network embedding algorithm based on deep reinforcement learning.

Virtual network embedding (VNE), as the key challenge of network resource management technology, lies in the contradiction between online embedding decision and pursuing long-term average revenue goals. Most of the previous work ignored the dynamics in Virtual Network (VN) modeling, or could not automatically detect the complex and time-varying network state to provide a reasonable network embedding scheme. In view of this, we model a network embedding framework where the topology and resource allocation change dynamically with the number of network users and workload, and then introduce a deep reinforcement learning method to solve the VNE problem. Further, a dynamic virtual network embedding algorithm based on Deep Reinforcement Learning (DRL), named DVNE-DRL, is proposed. In DVNE-DRL, VNE is modeled as a Markov Decision Process (MDP), and then deep learning is introduced to perceive the current network state through historical data and embedded knowledge, while utilizing reinforcement learning decision-making capabilities to implement the network embedding process. In addition, we improve the method of feature extraction and matrix optimization, and consider the characteristics of virtual network and physical network together to alleviate the problem of redundancy and slow convergence. The simulation results show that compared with the existing advanced algorithms, the acceptance rate and average revenue of DVNE-DRL are increased by about 25% and 35%, respectively.

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.

Machine Learning Requirements for Energy-Efficient Virtual Network Embedding

Network virtualization is a technology proven to be a key enabling a family of strategies in different targets, such as energy efficiency, economic revenue, network usage, adaptability or failure protection. Network virtualization allows us to adapt the needs of a network to new circumstances, resulting in greater flexibility. The allocation decisions of the demands onto the physical network resources impact the costs and the benefits. Therefore it is one of the major current problems, called virtual network embedding (VNE). Many algorithms have been proposed recently in the literature to solve the VNE problem for different targets. Due to the current successful rise of artificial intelligence, it has been widely used recently to solve technological problems. In this context, this paper investigates the requirements and analyses the use of the Q-learning algorithm for energy-efficient VNE. The results achieved validate the strategy and show clear improvements in terms of cost/revenue and energy savings, compared to traditional algorithms.

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.

Deep Reinforcement Learning Algorithm for Latency-Oriented IIoT Resource Orchestration

Due to geographical factors and resource constraints, the traditional Internet architecture cannot meet the needs of the space–air–ground-integrated network (SAGIN) resource layout in the Industrial Internet of Things (IIoT) service. How to arrange network resources in SAGIN quickly and efficiently to meet the quality of service requirements of users has become a hot research topic in the industry. Based on the characteristics of SAGIN with multiple network segments, we convert the resource scheduling problem of SAGIN into a multidomain virtual network embedding (VNE) problem. This article proposes a latency-sensitive VNE algorithm based on deep reinforcement learning (DDRL-VNE) in the SAGIN environment. Unlike traditional latency optimization algorithms, we consider the effect of traffic size and hop count on latency when evaluating latency. We constructed a learning agent composed of a five-layer policy network and extracted a feature matrix as its training environment based on the network attributes of SAGIN. The node embedding is completed according to the probability that each node is embedded in the training, and then the breadth-first search strategy is used to complete the link embedding. The experimental results effectively illustrate the effectiveness of the algorithm in the SAGIN resource allocation problem.

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%.

Trust-Aware Virtual Network Embedding in Wireless Sensor Networks

Network virtualization in wireless sensor networks (WSNs) enables the utilization of their shared sensing capabilities. Efficient assignment of WSN resources to maximize the infrastructure provider’s revenue can be achieved by virtual network embedding (VNE) while considering quality of information (QoI) (as the accuracy of sensing), quality of service (QoS) (as the reliability), and wireless interference handling constraints. Improving the acceptance ratio of VNE is essential because the more the virtual networks can be mapped onto the substrate network, the more revenue they will generate for the infrastructure provider. However, the shared and complex nature of VNE exposes the WSNs to security risks. This article develops a novel offline trust-aware virtual WSN (TA-VWSN) algorithm to maximize the virtual networks acceptance rate while minimizing the cost. This algorithm improves the QoI, QoS, and security by adding required trust level constraints to virtual nodes and links and trust level constraints to the substrate counterparts. Our algorithm embeds virtual nodes and links on substrate nodes and links with the required trust levels. The additional constraints increase the complexity of computation required to achieve an optimal solution. However, our TA-VWSN algorithm achieves a high-quality suboptimal solution in a short duration, enabling us to investigate the tradeoff between solution quality and search time. Our algorithm is also evaluated in large-scale network scenarios to verify all enforced limitations by the WSN substrate. While adding security constraints limits the acceptance ratio, the simulation results demonstrate its superiority in terms of average network throughput, measurement error efficiency, and processing time when the trust attributes are assigned, making the VNE algorithm more practical.

Non-Euclidean Graph-Convolution Virtual Network Embedding for Space–Air–Ground Integrated Networks

For achieving seamless global coverage and real-time communications while providing intelligent applications with increased quality of service (QoS), AI-enabled space–air–ground integrated networks (SAGINs) have attracted widespread attention from all walks of life. However, high-intensity interactions pose fundamental challenges for resource orchestration and security issues. Meanwhile, virtual network embedding (VNE) is applied to the function decoupling of various physical networks due to its flexibility. Inspired by the above, for SAGINs with non-Euclidean structures, we propose a graph-convolution virtual network embedding algorithm. Specifically, based on the excellent decision-making properties of deep reinforcement learning (DRL), we design an orchestration network combined with graph convolution to calculate the embedding probability of nodes. It fuses the information of the neighborhood structure, fully fits the original characteristics of the physical network, and utilizes the specified reward mechanism to guide positive learning. Moreover, by imposing security-level constraints on physical nodes, it restricts resource access. All-around and rigorous experiments are carried out in a simulation environment. Finally, results on long-term average revenue, VNR acceptance ratio, and long-term revenue–cost ratio show that the proposed algorithm outperforms advanced baselines.

Graph Convolutional Network Aided Virtual Network Embedding for Internet of Thing

The past few years have seen the dramatic adoption of the Internet of Things (IoT) in everyday life, from manufacturing to healthcare. With the emergence of various new Internet of Things applications, it is a challenging problem to meet the different QoS requirements of Internet of Things applications in shared substrate networks. Recently, Network Virtualization (NV) has attracted a large amount of attention from academia and industry. NV enables multiple virtual networks to coexist on the same substrate network, thus providing IoT users with customized end-to-end services. The main challenge of NV is the Virtual Network Embedding (VNE) problem, which refers to embed different virtual networks into one substrate network. Inspired by the recent success of graph convolutional network (GCN) in graph structured data processing, in this paper, we propose a GCN aided VNE algorithm. The GCN can extract high-order spatial structure information among substrate nodes through the convolution kernel. Considering that the training data of VNE has no label, we introduce the policy gradient algorithm to optimize the GCN model. In addition, three evaluation metrics are designed to evaluate the performance of the network embedding policy. Some simulations are implemented to evaluate our proposed algorithm in comparison to the other state-of-the-art solutions.

Deep Reinforcement Learning Based Edge Computing Network Aided Resource Allocation Algorithm for Smart Grid

The dramatic increase in the volume of users and services makes scheduling network resources for smart grids a key challenge. Network slicing is an important technology to solve this problem. We introduce edge computing networks into the smart grid to intelligently allocate resources based on users’ quality of service (QoS) and available resources. However, existing heuristic resource scheduling algorithms often lead to resource fragmentation and thus fall into local optima. To this end, we propose a deep reinforcement learning (DRL)-based virtual network embedding algorithm to optimize the resource allocation strategy of smart grids from a network virtualization perspective. We extract the network properties of the smart grid to construct a policy network as a training environment for DRL agents. Finally, DRL derives the probability of each node being embedded based on the extracted attributes of edge computing nodes and completes user request (UR) embedding based on this probability. The experimental results show that the algorithm proposed in this paper has excellent performance with guaranteed low latency, 21% improvement in long-term revenue and 5.6% improvement in UR success rate compared with the other two algorithms.

Multi-Domain Virtual Network Embedding Algorithm Based on Horizontal Federated Learning

Network Virtualization (NV) is an emerging network dynamic planning technique to overcome network rigidity. As its necessary challenge, Virtual Network Embedding (VNE) enhances the scalability and flexibility of the network by decoupling the resources and services of the underlying physical network. For future multi-domain physical network modeling with the characteristics of dynamics, heterogeneity, privacy, and real-time, the existing related works perform unsatisfactorily. Federated learning (FL) jointly optimizes the network by sharing parameters among multiple parties and is widely used to address data privacy and data silos. Aiming at the NV challenge of multi-domain physical networks, this work is the first to propose using FL to model VNE, and presents a VNE architecture based on Horizontal Federated Learning (HFL) (HFL-VNE). Specifically, combined with the distributed training paradigm of FL, we deploy local servers in each physical domain, which can effectively focus on local features and reduce resource fragmentation. A global server is deployed to aggregate and share training parameters, which enhances local data privacy and significantly improves learning efficiency. Furthermore, we deploy the Deep Reinforcement Learning (DRL) model in each server to dynamically adjust and optimize the resource allocation of the multi-domain physical network. In DRL-assisted FL, HFL-VNE jointly optimizes decision-making through specific local and federated reward mechanisms and loss functions. Finally, the superiority of HFL-VNE is proved by combining simulation experiments and comparing it with related works.

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.

Dynamic Virtual Network Embedding Algorithm Based on Graph Convolution Neural Network and Reinforcement Learning

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by the heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This article proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

Survivable virtual network embedding algorithm considering multiple node failure in IIoT environment

The Industrial Internet of Things is a new ecology formed by the deep integration of the Internet of Things and the industrial environment. Network is one of the core technologies of industrial Internet of things. However, in the face of billions of industrial Internet of Things applications, the traditional network architecture cannot meet the needs of these applications for network resources. Network virtualization technology can effectively solve the above problems by decoupling network services from the underlying network. However, the existing heuristic virtual network embedding algorithm is easy to fall into the local optimum, such as the virtual network embedding algorithm based on particle swarm optimization. Therefore, in order to solve the above problems, a virtual network embedding strategy based on the hybrid whale optimization algorithm is proposed. This strategy can avoid the introduction of the population into the local optimum while reducing the amount of calculation. Secondly, a node importance measurement strategy based on weighted aggregation coefficient and traffic prediction is introduced. This strategy can conduct a more reasonable and effective assessment of the importance of nodes. Thirdly, to reduce resource occupation and improve network security and stability, we propose a multi-node failure recovery strategy. It depends on the construction of candidate nodes and the selection of recovery points. Simulation results show that compared with other algorithms, the proposed algorithm reduces the cost and maintains a high success rate of fault recovery. In addition, its request acceptance rate is twice that of other algorithms.