Virtual Network Requests Research Articles

Column generation algorithms for virtual network embedding in flexi-grid optical networks.

Network virtualization provides means for efficient management of network resources by embedding multiple virtual networks (VNs) to share efficiently the same substrate network. Such virtual network embedding (VNE) gives rise to a challenging problem of how to optimize resource allocation to VNs and to guarantee their performance requirements. In this paper, we provide VNE algorithms for efficient management of flexi-grid optical networks. We provide an exact algorithm aiming to minimize the total embedding cost in terms of spectrum cost and computation cost for a single VN request. Then, to achieve scalability, we also develop a heuristic algorithm for the same problem. We apply these two algorithms for a dynamic traffic scenario where many VN requests arrive one-by-one. We first demonstrate by simulations for the case of a six-node network that the heuristic algorithm obtains very close blocking probabilities to exact algorithm (about 0.2% higher). Then, for a network of realistic size (namely, USnet) we demonstrate that the blocking probability of our new heuristic algorithm is about one magnitude lower than a simpler heuristic algorithm, which was a component of an earlier published algorithm.

A model for virtual network embedding using Artificial Bee Colony

SummaryIn a network virtualization environment, a significant research problem is that of virtual network embedding. As the network virtualization system is distributed in nature, an effective solution on how to optimally embed a dynamically generated virtual network request on the substrate networks that are owned and managed by multiple infrastructure providers needs proper attention. The problem is computationally hard, and therefore, many approaches, implying heuristics/meta‐heuristics, have been applied for the same. A meta‐heuristic, Artificial Bee Colony algorithm is getting popular due to its robustness toward complex problem solving. A novel approach based on Artificial Bee Colony to address the dynamic virtual network embedding problem in a multiple infrastructure provider scenario is proposed in this work. Bee population is initialized by using a greedy heuristic in which the number of substrate networks together with virtual network requests constructs a bee. Generated solution, in the population, is improvised by using greedy selection that explores a local search method adopted by the bees. In greedy selection, the new candidate source is memorized by the bee if its fitness is better than the fitness of the existing source. The performance study of the proposed model is done by simulation over various metrics such as embedding cost, embedding time, and acceptance ratio. A comparative study is conducted with other nature‐inspired virtual network embedding algorithms on these metrics. The findings affirm that the proposed virtual network embedding approach performs well and produces better results.

Reliable design for virtual network requests with location constraints in edge-of-things computing

How to efficiently map virtual networks (VNs) onto a shared physical network is a challenging issue in the field of network virtualization in edge-of-things computing. Since an efficient VN mapping approach can reduce network resource consumption, lower latency, and enhance service reliability, it is important for both customers and network service providers. In this paper, we study the problem of mapping multiple VNs with geographic location constraints onto a physical network while considering the survivability and reliability requirements of each VN request in edge-of-things based data centers. We present the model of this problem and propose a Geographic-Guided Survivable Multiple VN Mapping (GG-SMVNM) algorithm to efficiently solve this problem, which simultaneously considers resource sharing and mapping VN links and nodes in edge-of-things computing. Furthermore, we conduct a large amount of simulations to validate and evaluate our proposed approach. The simulation results show that the proposed method is superior to the existing solution.

Toward efficient dynamic virtual network embedding strategy for cloud networks

Network virtualization is one of the most promising technologies for future networking and considered as a critical information technology resource that connects distributed, virtualized cloud computing services and different components such as storage, servers, and application. Network virtualization allows multiple virtual networks to coexist on the same shared physical infrastructure simultaneously. One of crucial factors in network virtualization is virtual network embedding which provisions a method to allocate physical substrate resources to virtual network requests. In this article, we investigate virtual network embedding strategies and related issues for resource allocation of an Internet provider to efficiently embed virtual networks that are requested by virtual network operators who share the same infrastructure provided by the Internet provider. In order to achieve that goal, we design a heuristic virtual network embedding algorithm that simultaneously embeds virtual nodes and virtual links of each virtual network request onto the physic infrastructure. Via extensive simulations, we demonstrate that our proposed scheme significantly improves the performance of virtual network embedding by enhancing the long-term average revenue as well as acceptance ratio and resource utilization of virtual network requests compared to prior algorithms.

Virtual network embedding in cross-domain network based on topology and resource attributes

Aiming at the network architecture ossification and the diversity of access technologies issues, this paper researches the cross-domain virtual network embedding algorithm. By analysing the topological attribute from the local and global perspective of nodes in the virtual network and the physical network, combined with the local network resource property, we rank the embedding priority of the nodes with PCA and TOPSIS methods. Besides, the link load distribution is considered. Above all, We proposed an cross-domain virtual network embedding algorithm based on topology and resource attributes. The simulation results depicts that our algorithm increases the acceptance rate of multi-domain virtual network requests, compared with the existing virtual network embedding algorithm.

A Novel Optimal Mapping Algorithm With Less Computational Complexity for Virtual Network Embedding

Network virtualization (NV) is widely accepted as one enabling technology for future network, which enables multiple virtual networks (VNs) with different paradigms and protocols to coexist on the shared substrate network (SN). One key challenge in NV is VN embedding (VNE), which maps a VN onto the shared SN. Since VNE is NP-hard, existing efforts mainly focus on proposing heuristic algorithms that try to achieve feasible VNE in reasonable time, consequently the resulted embedding is not optimal. To tackle this difficulty, we propose a candidate assisted (CAN-A) optimal VNE algorithm with lower computational complexity. The key idea of the CAN-A algorithm lies in constructing the candidate substrate node subset and the candidate substrate path subset before embedding. This reduces the mapping execution time substantially without performance loss. In the following embedding, four types of node and link constraints are considered in the CAN-A algorithm, making it more applicable to realistic networks. Simulation results show that the execution time of CAN-A is hugely cut down compared with pure VNE-MIP algorithm. CAN-A also outperforms the typical heuristic algorithms in terms of other performance indices, such as the average VN request acceptance ratio and the average virtual link propagation delay.

Joint load balancing and energy saving algorithm for virtual network embedding in infrastructure providers

Network virtualization is key to cloud services, in that it enables multiple users to share a physical infrastructure through abstraction. We propose an online virtual network (VN) embedding scheme which jointly considers load balancing and energy saving so as to maximize the profit of Infrastructure Providers (InPs). For load balancing, we propose to minimize a convex objective which penalizes mapping of VNs to overloaded resources. For energy saving, we consider two popular energy models: speed scaling and power-down. In the speed scaling model, energy consumption is modeled as a convex function of the load imposed on resources. We observe that both load-balancing and energy-saving objectives superadditively penalize high utilization/congestion at resources, and that such synergistic nature of the objectives leads to efficient joint optimization. In the power-down model, a fixed cost exists for keeping a node powered on, which is characterized by a nonconvex energy curve. In this case, we propose an iterative algorithm which explores the trade-offs between load balancing versus cost reduction from power-down of idle servers, in a controlled way. Our algorithm performs a sequential node and link mapping; in particular, for link mapping, we adopt randomized rounding with path stripping in order to obtain a constant factor approximation to the minimum penalty for link utilization. Numerical experiments show the efficacy of our algorithm in servicing VN requests of various topologies and resource requirements.

A novel reinforcement learning algorithm for virtual network embedding

Abstract Network virtualization enables the share of a physical network among multiple virtual networks. Virtual network embedding determines the effectiveness of utilization of network resources. Traditional heuristic mapping algorithms follow static procedures, thus cannot be optimized automatically, leading to sub-optimal ranking and embedding decisions. To solve this problem, we introduce a reinforcement learning method to virtual network embedding. In this paper, we design and implement a policy network based on reinforcement learning to make node mapping decisions. We use policy gradient to achieve optimization automatically by training the policy network with the historical data based on virtual network requests. To the best of our knowledge, this work is the first to utilize historical requests data to optimize network embedding automatically. The performance of the proposed embedding algorithm is evaluated in comparison with two other algorithms which use artificial rules based on node ranking. Simulation results show that our reinforcement learning is able to learn from historical requests and outperforms the other two embedding algorithms.

Fiber-Wireless Network Virtual Resource Embedding Method Based on Load Balancing and Priority

Network virtualization is becoming one of the most promising ways to solve resource solidification problem of fiber-wireless access networks. To fully utilize substrate resources, a virtual resource embedding method including three sub-mechanisms is presented in this paper. The first one is the priority-based virtual network request (VNR) selecting mechanism, which sorts VNRs and preferentially selects higher priority VNRs for embedding. Then a dynamic embedding mechanism based on load balancing is presented to increase the acceptance rate of VNRs. Second, when embedding is saturated, an embedding update mechanism is proposed to deal with the arrival of new VNRs. It promotes the acceptance rate of high-priority VNRs and the revenue of the infrastructure provider (InP). Finally, a backup resource sharing mechanism is designed to improve reliability, which enables backup resources to be shared and responds to the failures with minimal resources. The simulation results show that the proposed embedding method can balance the resource consumption better than the Greedy Node Embedding and Shortest-path Link Embedding Algorithm, as well as the Greedy Node Embedding and Maximum Residual Bandwidth Link Embedding Algorithm. Besides, by applying our proposed algorithm, higher VNR acceptance rate, InP revenue, and resource utilization are achieved.

Collaborative Dynamic Virtual Network Embedding Algorithm Based on Resource Importance Measures

Many existing virtual network embedding (VNE) algorithms adopt the greedy embedding strategies, which embed the in-progress virtual network requests (VNRs) onto the substrate nodes and links with more residual resources. However, the previous VNRs may overconsume the critical substrate resources and lead to the resource fragmentation problem in the substrate networks, thus reducing the acceptance ratio of the latter VNRs. To address this problem, we propose a novel collaborative VNE algorithm based on resource importance measures, RIM-ViNE, in which the global node importance and link importance in the initial substrate network are measured using multiple topological attributes and are used to set the embedding cost of VNRs. Then, the VNE problem is defined as a linear programming model and is solved by minimizing the total embedding cost, which could bring about the coordinative embedding between different VNRs, and thus preventing the critical resources from being over occupied by the previous VNRs with small requirements and improving the resource fragmentation problem. Moreover, we propose a dynamic reconfiguration mechanism based on critical nodes protection (CNP-Re) to improve the resource fragmentation problem further. Extensive experiments are conducted under two network scenarios, and the results show that the proposed algorithms outperform the VNE algorithms that only consider the coordination between node mapping and link mapping or only measure the node importance in the residual substrate network, and the average VNR acceptance ratio, average revenue, and average resource utilization ratio are effectively improved.

A Virtual Network Embedding Algorithm Based on Cellular Automata Genetic Mechanism

The optimal embedding problem of virtual network requests, which satisfies nodes and link constraints, is a NP-hard problem. Heuristic algorithms solve the problem with the mathematical model optimization, but it fails to consider the influence of the virtual network embedding node itself on the optimal solution. So the cellular automata genetic mechanism is introduced into the problem, and the virtual network embedding algorithm based on cellular genetic algorithm (VNE-CGA) has been proposed. VNE-CGA uses the cellular automata to model the node, and replaces the "B4567/S1234" rule with the crossover operation in genetic algorithm. Through learning from neighbours to guide the individual's optimization process, VNECGA improves the inherent defects of traditional genetic algorithm. The experimental results show that the request acceptance ratio and the long-term average revenue increase about 5% and 12%.

A Survey on Algorithmic Aspects of Virtual Optical Network Embedding for Cloud Networks

An important challenge in network virtualization is the process of assigning physical resources to virtual network requests, where virtual node resources represent the demands of IT capacities while virtual links are connected between pairs of virtual nodes. Due to their special characteristics, physical optical network infrastructures requires specific strategies to solve this problem, called virtual optical network embedding (VONE). This paper presents a survey on most relevant VONE schemes found in the literature. A discussion of important algorithmic aspects of this problem is presented, such as optical-layer restrictions, global performance metrics, and possible research directions. In addition, we propose a new taxonomy of VONE approaches, considering the type of optical networks, dynamic or static scenarios, and the optimization method.

Energy-aware virtual network embedding in flexi-grid networks.

Network virtualization technology has been proposed to allow multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network, which increases the utilization of the substrate network. Efficiently mapping VNs on the substrate network is a major challenge on account of the VN embedding (VNE) problem. Meanwhile, energy efficiency has been widely considered in the network design in terms of operation expenses and the ecological awareness. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the electricity cost of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low electricity cost. Numerical results show that the heuristic algorithm performs closely to the ILP for a small size network, and we also demonstrate its applicability to larger networks.

Virtual network embedding based on modified genetic algorithm

To fend off the ossification of Internet architecture, virtual network embedding has been propounded as one of the most important techniques to address this issue. Virtual network embedding is a process that consists of two stages including node mapping stage and link mapping stage, the aim of node mapping stage is to map the virtual nodes from virtual network requests (VNRs) onto the substrate nodes meanwhile satisfying the CPU capacity constraints on nodes, the goal of link mapping stage is to map the virtual links from VNRs onto the substrate paths while satisfying the bandwidth resource constraints on links. This paper proposed a virtual network embedding algorithm based on modified genetic algorithm, improved the classical genetic algorithm from three aspects: population initialization strategy, improved mutation operation and improvement operation, took advantage of the selection operation, crossover operation, mutation operation, feasibility checking operation, and utilized the fitness function to choose the best chromosome. Simulation results indicated that our proposed method has significantly increased the acceptance ratio of VNRs and the long-term average revenue of Infrastructures (InPs) compared with other two state-of-the-art algorithms.

Efficient virtual network embedding algorithm based on restrictive selection and optimization theory approach

Network virtualization (NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks (VNs) with different protocols to coexist on a shared substrate network (SN). One main challenge in NV is virtual network embedding (VNE). VNE is a NP-hard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in small-scaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT (Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNE-RSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNE-RSOT, a simulation against typical and state-of-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request (VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.

Virtual Network Embedding based on Node Connectivity Awareness and Path Integration Evaluation

As a main challenge in network virtualization, virtual network embedding problem is increasingly important and heuristic algorithms are of great interest. Aiming at the problems of poor correlation in node embedding and link embedding, long distance between adjacent virtual nodes and imbalance resource consumption of network components during embedding, we herein propose a two-stage virtual network embedding algorithm NA-PVNM. In node embedding stage, resource requirement and breadth first search algorithm are introduced to sort virtual nodes, and a node fitness function is developed to find the best substrate node. In link embedding stage, a path fitness function is developed to find the best path in which available bandwidth, CPU and path length are considered. Simulation results showed that the proposed algorithm could shorten link embedding distance, increase the acceptance ratio and revenue to cost ratio compared to previously reported algorithms. We also analyzed the impact of position constraint and substrate network attribute on algorithm performance, as well as the utilization of the substrate network resources during embedding via simulation. The results showed that, under the constraint of substrate resource distribution and virtual network requests, the critical factor of improving success ratio is to reduce resource consumption during embedding.

Fault Tolerant Algorithms for Multiple Infrastructure Provider Cooperation in Network Virtualization Environment Based on Auctioning

The multiple infrastructure provider network virtualization system is a self-governing system that independently figures out the appropriate number of infrastructure providers and selects the most suitable infrastructure providers in the group to map the virtual network request. This work applies a fault tolerant market-based strategy for efficient working of a network virtualization environment by cooperation of multiple infrastructure providers while dealing with restricted or absolute failures of infrastructure providers during embedding in an unknown, random and dynamic virtual network system. The approach uses auctioning mechanism to decide on the infrastructure provider to serve the virtual network requests, sent by the service providers. A request can be mapped either by a single infrastructure provider or cooperatively by multiple infrastructure providers, depending on the requirements of the service provider’s request and the network resources available with the infrastructure provider. The infrastructure provider that matches best the demands of the service provider is chosen by the system. The feasibility of the proposed methodology is tested by implementing the approach on a group of multiple infrastructure providers that participate in the auctioning mechanism to serve multiple service providers’ virtual network requests.

A Markov Reward based Resource-Latency Aware Heuristic for the Virtual Network Embedding Problem

An ever increasing use of virtualization in various emerging scenarios, e.g.: Cloud Computing, Software Defined Networks, Data Streaming Processing, asks Infrastructure Providers (InPs) to optimize the allocation of the virtual network requests (VNRs) into a substrate network while satisfying QoS requirements. In this work, we propose MCRM, a two-stage virtual network embedding (VNE) algorithm with delay and placement constraints. Our solution revolves around a novel notion of similarity between virtual and physical nodes. To this end, taking advantage of Markov Reward theory, we define a set of metrics for each physical and virtual node which captures the amount of resources in a node neighborhood as well as the degree of proximity among nodes. By defining a notion of similarity between nodes we then simply map virtual nodes to the most similar physical node in the substrate network. We have thoroughly evaluated our algorithm through simulation. Our experiments show that MCRM achieves good performance results in terms of blocking probability and revenues for the InP, as well as a high and uniform utilization of resources, while satisfying the delay and placement requirements.

A model for virtual network embedding across multiple infrastructure providers using genetic algorithm

Network virtualization is an important aspect in cloud computing, where network is assumed to be consisting of infinite amount of nodes and links. The substrate network (physical network), has limited capacity and so virtual network embedding on the substrate network becomes a problem. Virtual network embedding is a computationally hard problem, considering various constraints on nodes and links. The proposed work applies Genetic Algorithm for the virtual network embedding problem for mapping multiple virtual network requests on infrastructure providers managing multiple substrate networks. Performance evaluation, through simulations, indicates that the proposed model preforms better for the performance metrics such as infrastructure provider revenue, acceptance ratio and node and link utilization in comparison to few other contemporary mapping models.

Virtual Network Embedding via Monte Carlo Tree Search.

Network virtualization helps overcome shortcomings of the current Internet architecture. The virtualized network architecture enables coexistence of multiple virtual networks (VNs) on an existing physical infrastructure. VN embedding (VNE) problem, which deals with the embedding of VN components onto a physical network, is known to be -hard. In this paper, we propose two VNE algorithms: MaVEn-M and MaVEn-S. MaVEn-M employs the multicommodity flow algorithm for virtual link mapping while MaVEn-S uses the shortest-path algorithm. They formalize the virtual node mapping problem by using the Markov decision process (MDP) framework and devise action policies (node mappings) for the proposed MDP using the Monte Carlo tree search algorithm. Service providers may adjust the execution time of the MaVEn algorithms based on the traffic load of VN requests. The objective of the algorithms is to maximize the profit of infrastructure providers. We develop a discrete event VNE simulator to implement and evaluate performance of MaVEn-M, MaVEn-S, and several recently proposed VNE algorithms. We introduce profitability as a new performance metric that captures both acceptance and revenue to cost ratios. Simulation results show that the proposed algorithms find more profitable solutions than the existing algorithms. Given additional computation time, they further improve embedding solutions.