Virtual Network Requests Research Articles

Agent-based distributed protocol for resource discovery and allocation of virtual networks over elastic optical networks

Network virtualization is a key enabling technology for “Infrastructure as a Service” provisioning, increasing the flexibility and cost savings offered to customers. By extending the concept of server virtualization to the network infrastructure, the allocation of different, independent virtual networks over a single physical network is carried out on demand. A fundamental challenge in network virtualization systems is to choose which physical nodes and links to use for hosting virtual networks in the physical infrastructure, known as the “virtual network allocation” problem. All virtual network allocation proposals on elastic optical networks assume a centralized operation, deploying a single node with access to the network state global information and assigning resources accordingly. However, such configuration might exhibit the inherent problems of centralized systems: survivability and scalability. In this paper, we present a distributed protocol for resource discovery, mapping, and allocation of network virtualization systems. The distributed protocol is generic enough as to be used with different substrate networks. However, in this paper, it has been adapted to work over an elastic optical network infrastructure, where further considerations regarding the spectrum continuity and contiguity constraints must also be taken into account. The distributed protocol is based on the concept of alliances: upon the arrival of a virtual network request, agents located in the physical network nodes compete to form the first alliance able to host the virtual network. Because the first alliances to be formed are also the ones composed by nearby nodes, a good network resource usage is achieved. The feasibility of the distributed protocol was studied by evaluating its ability to successfully establish virtual networks within acceptable time and with low bandwidth consumption from the coordination messages.

Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning

Wireless network virtualization is widely used to solve the ossification problem of networks, such as 5G and the Internet of Things. The most crucial method of wireless network virtualization is virtual network embedding, which allows virtual networks to share the substrate network resources. However, in wireless networks, link interference is an inherent problem while mapping virtual networks because of the characteristics of wireless channels. To distribute resources efficiently and address the problem of interference, a dynamic embedding algorithm with deep reinforcement learning is proposed. During the training stage, we take resource use and interference from substrate networks as observations to train the agent, and then the agent generates a resource allocation strategy. Aiming at realizing load balance, we reshape the reward function considering the execution ratio and residual ratio of substrate network resources as well as the cost consumed by current virtual network request. Numerical tests show that our embedding approach increases the acceptance ratio and maintains better robustness. Moreover, the results also illustrate that our algorithm maintains a high acceptance ratio while producing less interference and lower cost.

A dynamic and resource sharing virtual network mapping algorithm

Network virtualization can effectively establish dedicated virtual networks to implement various network functions. However, the existing research works have some shortcomings, for example, although computing resource properties of individual nodes are considered, node storage properties and the network topology properties are usually ignored in Virtual Network (VN) modelling, which leads to the inaccurate measurement of node availability and priority. In addition, most static virtual network mapping methods allocate fixed resources to users during the entire life cycle, and the users’ actual resource requirements vary with the workload, which results in resource allocation redundancy. Based on the above analysis, in this paper, we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE, first, we construct a new, more realistic network framework in which the properties of nodes include computing resources, storage resources and topology properties. In the node mapping process, three properties of the node are used to measure its mapping ability. Second, we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties, so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link mapping. Finally, we divide the resource requirements of Virtual Network Requests (VNRs) into basic sub-requirements and variable sub-variable requirements to complete dynamic resource allocation. The former represents monopolizing resource requirements by the VNRs, while the latter represents shared resources by many VNRs with the probability of occupying resources, where we keep a balance between resource sharing and collision among users by calculating the collision probability. Simulation results show that the proposed NMA-PRS-VNE can increase the average acceptance rate and network revenue by 15% and 38%, and reduce the network cost and link pressure by 25% and 17%.

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.

Resource Fragmentation-Aware Embedding in Dynamic Network Virtualization Environments

In network virtualization environments, with random arrival and departure of virtual network requests, there exist some resources (i.e., link resources and node resources) isolated from others in substrate networks. This phenomenon is referred to as resource fragmentation. In this paper, we attempt to improve the resource utilization efficiency by avoiding resource fragmentation in substrate networks. First and most importantly, we define a new metric called resource fragmentation degree (RFD) to quantitatively measure the status of resource fragmentation at substrate nodes and links. The basic idea of RFD is that the resource availability of a node (or a link) is determined by the residual link and node resources around the node (or the link). Based on the definition of RFD, we formulate the virtual network embedding (VNE) problem as a mixed integer programming problem with consideration of the cost of resource fragmentation. Then, an online VNE algorithm with consideration of RFD (VNERFD) is proposed to solve the problem, which is performed according to the current resource status of substrate networks and virtual network requests. To reduce accumulated fragmented resources produced by dynamic arrival and departure of virtual network requests, a heuristic virtual network reconfiguration algorithm based on RFD (VNR-RFD) is proposed. Simulation results show that VNE-RFD and VNR-RFD can effectively reduce fragmented resources and thus embed more virtual networks into substrate networks.

Maximizing Virtual Network Embedding Requests in RF-Charging IoT Networks

This letter considers the problem of embedding the maximum number of Virtual Network Requests (VNRs) in an Internet of Things (IoT) network with Power Beacons (PBs). It presents a Mixed Integer Linear Program (MILP) and a heuristic to determine the transmit power allocation of PBs, mappings of virtual nodes and edges onto devices and links, and a link schedule to provision bandwidth to support traffic on virtual edges. Our results show the proposed heuristic attains 90.31% of MILP’s performance.

VNE-HRL: A Proactive Virtual Network Embedding Algorithm Based on Hierarchical Reinforcement Learning

Virtual network embedding (VNE) that instantiates virtualized networks on a substrate infrastructure, is one of the key research problems for network virtualization. Most existing VNE approaches, however, focus on the current virtual network request (VNR) and treat all VNRs equally, which disregard the long-term impact and waste many resources on the process of embedding infeasible VNRs (i.e., VNRs that cannot be embedded completely). To address these problems, a proactive virtual network embedding algorithm based on hierarchical reinforcement learning, VNE-HRL, is proposed in this paper. Within our framework, the VNE task is performed by a two-level agent that considers both the long-term impact of a VNR and the short-term effect of an embedding action. For each processing, a high-level agent aims to select a currently feasible VNR with the maximum long-term reward from a window-based batch, and a low-level agent is assigned to embed the selected VNR on a substrate infrastructure by performing a series of embedding actions. Extensive simulation results indicate that our algorithm best performance on most metrics compared with existing state-of-the-art solutions, with up to 9.92% and 33.03% improvement on acceptance ratio and average revenue.

A highly reliable embedding algorithm for airborne tactical network virtualization

The evolution of airborne tactical networks (ATNs) is impeded by the network ossification problem. As a solution, network virtualization (NV) can provide a flexible and scalable architecture where virtual network embedding (VNE) is a key part. However, existing VNE algorithms cannot be optimally adopted in the virtualization of ATN due to the complex interference in air-combat field. In this context, a highly reliable VNE algorithm based on the transmission rate for ATN virtualization (TR-ATVNE) is proposed to adapt well to the specific electromagnetic environment of ATN. Our algorithm coordinates node and link mapping. In the node mapping, transmission-rate resource is firstly defined to effectively evaluate the ranking value of substrate nodes under the interference of both environmental noises and enemy attacks. Meanwhile, a feasible splitting rule is proposed for path splitting in the link mapping, considering the interference between wireless links. Simulation results reveal that our algorithm is able to improve the acceptance ratio of virtual network requests while maintaining a high revenue-to-cost ratio under the complex electromagnetic interference.

Efficient Virtual Network Embedding of Cloud-Based Data Center Networks into Optical Networks

The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation. Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth. This article focuses on efficient embedding of data centers into optical networks, which aims to reduce complexity of the network topology by using the parallel transmission characteristics of optical fiber. We first present a novel virtual network embedding (VNE) mathematical model used for optical data center networks. Then we derive a priority of location VNE algorithm according to node proximity sensing and path comprehensive evaluation. Furthermore, we propose routing and wavelength assignment for DCNs into optical networks, and identify the lower bound of the required number of wavelengths. Extensive evaluations show that the proposed embedding algorithm can reduce the average waiting time of virtual network requests by 20 percent, increase the request acceptance rate and revenue-overhead ratio by 13 percent, as compared to the latest VNE algorithm.

A QoS-Based Splitting Strategy for a Resource Embedding Across Multiple Cloud Providers

In cloud computing, a fundamental management problem with the Infrastructure as a Service model lies in the efficient embedding of computing and networking resources onto distributed virtualized infrastructures. This issue, usually referred to as the Virtual Network Embedding (VNE) problem, has been well studied for a single Cloud Provider (CP). However, wide-area services delivery may require to embed heterogeneous resources over multiple CPs. This adds more complexity and scalability issues, since the Virtual Network Requests (VNRs) embedding process requires two phases of operation: the multicloud VNRs splitting, followed by the intracloud VNR segments mapping. This paper addresses the problem of VNE across multiple CPs by proposing a VNRs splitting strategy which aims at improving the performance and QoS of resulting VNR segments. An Integer Linear Program (ILP) is used to formalize the splitting phase as a maximization problem with constraints. Subsequently, in order to minimize the overall delay, a multi-objective intracloud resource mapping approach formalized as a Mixed-Integer Linear Program (MILP) is adopted. Simulations with the exact method show the efficiency of the proposed strategy based on several performance criteria. In particular, the acceptance rate and the delay are respectively improved by 15.1 and 18.5 percent, while preventing QoS violations.

Comparison of D-Vine and R-Vine Techniques for Virtual Network Embedding Problem

In Network virtualization, Virtual Network Embedding(VNE) is the process of mapping virtual nodes and links of a virtual network request(VNR) on a substrate network to fulfill the demands of the request. Embedding virtual network requests helps in achieving network virtualization efficiently. This paper presents Vineyard, a set of VN embedding algorithms namely D-Vine and R-Vine, to introduce a finer correlation between the node mapping and link mapping phases. Deterministic Virtual Network Embedding(D-Vine) algorithm is used to embed virtual nodes on to substrate network based on the capacity constraint when it is not satisfied the system takes the Randomised Virtual Network Embedding(R-Vine) algorithm to map the nodes based on location constraint. Subsequently, after the nodes are embedded the link mapping phase is done based on the distance constraint. A window-based virtual embedding algorithm(W-Vine) is also introduced to evaluate the effect of lookahead in Virtual Network Embedding. After the mapping of multiple virtual network requests, analysis is done to compare the node and CPU utilization in both the algorithms and the variations are conserved.

Virtual Network Provisioning over Mixed-Fixed/Flexible-Grid Optical Infrastructures

With the emergence of cloud services based on data centers, demands for bandwidth-intensive applications have increased dramatically, and application services have transferred to a more diversified direction. Management as well as capacity of the backbone network needs further development to catch up with rapidly evolved application demands. Optical network virtualization can facilitate the sharing of physical infrastructure among multiple network applications. Virtual Network Embedding (VNE), the main implementation of network virtualization, determines how to map a virtual network request onto physical substrate. To expand the network capacity, flexible-grid elastic optical networks have been considered as a promising supporting technology for the future infrastructure of the next-generation Internet. However, due to the expense of key enabling equipment for flexible grid optical networks, the brown-field migration from a fixed grid to a flexible grid gave birth to the co-existing fixed/flexible grid. Based on the co-existing fixed/flexible grid optical networks, we investigate the problem of Virtual Optical Network (VON) provisioning, and present a flexible-grid-aware virtual network embedding algorithm to map the virtual networks onto the substrate network. In addition, the performance of the algorithm was evaluated under four different network scenarios. Simulation results show that the proposed algorithm can achieve better performance in all four scenarios.

Dynamic Virtual Network Embedding of Mobile Cloud System Based on Global Resources in Internet of Vehicles

The Internet of Vehicles (IoV) has a typical Internet of things architecture, which is the specific application of Internet of things (IoT) technology in the field of intelligent transportation. Virtual network embedding (VNE) is the effective allocation of physical network resources to the virtual network, and is a virtualization technology widely used in the data center of the IoV. The existing VNE algorithm ignores the attributes of the network itself. It only allocates resources according to the order in which the requests arrive, resulting in the problem of low utilization of physical resources. In this paper, we propose an efficient topology control scheme for mobile nodes in IoVs. We firstly present a novel hierarchy-based dynamic VNE model for IoV. Then a VNE algorithm based on hierarchical priority is established by considering the attributes of virtual network requests. Finally, a global resource capacity based dynamic VNE algorithm is designed. Compared with precious VNE algorithms, the proposed algorithm has a 19 percent to 23 percent increase in the resource utilization rate of nodes, and elevates the overall request acceptance rate and revenue overhead, and can reduce the average waiting time of virtual network requests.

An Efficient Approach Based on Ant Colony Optimization and Tabu Search for a Resource Embedding Across Multiple Cloud Providers

In cloud computing, a fundamental management problem with the Infrastructure as a Service (IaaS) model lies in the efficient embedding of computational and networking resources onto distributed virtualized infrastructures owned by independent cloud providers (CPs). In such a context, this issue usually referred to as the Virtual Network Embedding (VNE) problem, adds more complexity since the entire embedding process requires two mayor phases of operation: the multicloud virtual network requests (VNRs) splitting, followed by the intracloud VNR segments mapping. This paper focuses on the splitting phase problem, by proposing a VNRs splitting strategy formalized as an Integer Linear Program (ILP) model, with the objective of improving the performance and QoS of resulting mapped VNR segments, while minimizing the resource provisioning expenditures. As the VNE is classified as an NP-hard problem, a hybrid metaheuristic approach based on the Ant Colony Optimization (ACO) combined with the Tabu Search (TS) as local search operator, is proposed in order to find good feasible solutions in reasonable time. The simulation results show the efficiency of the proposed approach, which generates, in a highly reduced computing time, solution costs very close to the exact solution, with an average cost gap ranging from 0 percent to a maximum of 3.42 percent.

RDAM: A Reinforcement Learning Based Dynamic Attribute Matrix Representation for Virtual Network Embedding

Network virtualization makes it possible to manage multiple virtual networks simultaneously on substrate physical networks. Virtual network embedding (VNE) is the critical step of network virtualization that maps virtual network requests to substrate physical networks. The majority of current virtual network embedding algorithms utilize heuristic algorithm, and manually customize a series of rules and assumptions. Therefore, these experimental results are not particularly convincing. This paper proposes a reinforcement learning based dynamic attribute matrix representation (RDAM) algorithm for virtual network embedding. The RDAM algorithm decomposes the process of node mapping into the following three steps: (1) static representation of substrate physical network. (2) dynamic update of substrate physical network. (3) Reinforcement-Learning-Based algorithm. To our best knowledge, RDAM algorithm is the first algorithm to apply spectral analysis and perturbation theory to virtual network embedding. Meanwhile, the method training virtual network embedding algorithm by reinforcement learning is also non-trivial. Furthermore, we compare RDAM algorithm with three other virtual network embedding algorithms. The results show that RDAM algorithm outperforms the other three algorithms in terms of several evaluation metrics, such as long-term average revenue, long-term revenue consumption ratio, and acceptance ratio.

A Load-Balanced Re-Embedding Scheme for Wireless Network Virtualization

In wireless network virtualization processes, load imbalance in substrate nodes and substrate links may significantly reduce the number of virtual network requests served by a substrate network. In addition, load imbalance also impairs resource utilization and increases embedding cost of the virtual networks. To tackle these issues, this work proposes a re-embedding scheme for virtual node (RSVN). Based on the load condition of a substrate network, RSVN first determines whether to start re-embedding procedure or not, and then selects proper re-embedded virtual nodes according to their re-embedding factors. Next, RSVN re-embeds the selected virtual nodes to balance the loads of a wireless substrate network. The extensive simulation results showed that RSVN improves the acceptance ratio of virtual network requests, increases the resource utilization of substrate networks, and reduces the average embedding cost of the entire virtual networks.

Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems

Virtual Network Embedding (VNE), which provides methods to assign multiple Virtual Networks (VN) to a single physical Substrate Network (SN), is an important task in network virtualization. The main problem in VNE is the efficiency of assigning customers’ virtual network requests to the substrate network. This problem is known to be a Non-deterministic Polynomial-time hard (NP-hard) and heuristic solutions have been developed to solve this kind of problem. The current trend toward Software-Defined Networking (SDN) has allowed new possibilities in virtual network embedding. In this work, we propose a distributed virtual network embedding for SDNs called DVSDNE using multi-agent systems. This framework could be used to run a centralized VNE algorithm in a distributed manner to scale these algorithms with respect to network size. DVSDNE uses agents to spread the load across the substrate network. Our simulation results show the effectiveness of the proposed algorithm. Results show that DVSDNE improves execution time of embedding algorithms in large scale substrate networks, while embedding results such as acceptance ratio, revenue to cost ratio, average latency to controller, and maximum latency to controller remain comparable.

RETRACTED: A Global Optimal Mapping Method of Network based on Discrete Optimization Firefly Algorithm

In order to solve the current network rigidity and optimize the resource requirements of multiple virtual networks for synchronous mapping, improve the success rate of virtual network mapping requests, the long-term revenue and overhead ratio of the substrate network, node resource utilization rate and link resource utilization rate. A global optimal mapping method based on discrete optimization firefly algorithm is presented. Analyze the problem of virtual network mapping, map virtual nodes to physical nodes, and map virtual links to physical paths. According to the resource constraints of the virtual network and the substrate network, a multi-objective optimization model of the virtual network mapping is constructed, and the discrete fireflies optimization algorithm is used to obtain the global optimal solution of the virtual network mapping model to achieve the optimal allocation of global resources. The experimental results show that the discrete optimization firefly algorithm has a good performance in solving the virtual network mapping problem, and can effectively improve the virtual network request acceptance rate, node resource utilization rate, link resource utilization rate, and long-term revenue and cost ratio of the substrate network, ensuring Optimization of virtual network resources.

Resource competition in virtual network embedding

We consider a virtual network (VN) embedding problem on a large-scale substrate physical network. We permit varying capacities and cost rates, and reservation of resources (physical links and nodes) for more profitable later VN requests. Our aim is to maximize the long-run average revenue by controlling the allocation of physical components to arriving VN requests. We propose an index policy that selects, according to state-dependent indices, a set of available physical components for each arrival. The indices are calculated in closed form requiring only intrinsic off-line information about physical components and physical resource requirements. Under reasonable assumptions related to rapidly increasing demands for VNs and resource competition, we show that this proposed policy is asymptotically optimal when the lifespans of embedded virtual components are exponentially distributed. Extensive numerical results demonstrate that the long-run average revenue earned by the proposed index policy rapidly approaches optimality: performance deviations between the index policy and the optimal solution are less than 5% in most of our simulations even for relatively small systems. Our numerical results also indicate that the long-run performance of this policy is relatively insensitive to the form of the lifespan distributions.

IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode

Wireless network communication has developed rapidly in recent years, especially in the field of Internet of vehicles (IoV). However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. We focus on the urgent need of users for bandwidth resources in the IoV scenario under virtual network environment. This paper proposes a bandwidth aware multi domain virtual network embedding (BA-VNE) algorithm. The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode, and solves the problem of bandwidth resource allocation from the perspective of virtual network embedding (VNE). In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm to optimize the performance of the algorithm. In order to verify the effectiveness of the algorithm, we have carried out simulation experiments from link bandwidth, mapping cost and virtual network request (VNR) acceptance rate. The final results show that the proposed algorithm is better than other representative algorithms in the above indicators.