An efficient and coordinated mapping algorithm in virtualized SDN networks

Software-defined networking (SDN) enables the network virtualization through SDN hypervisors to share the underlying physical SDN network among multiple logically isolated virtual SDN networks (vSDNs), each with its own controller. The vSDN embedding, which refers to mapping a number of vSDNs to the same substrate SDN network, is a key problem in the SDN virtualization environment. However, due to the distinctions of the SDN, such as the logically centralized controller and different virtualization technologies, most of the existing embedding algorithms cannot be applied directly to SDN virtualization. In this paper, we consider controller placement and virtual network embedding as a joint vSDN embedding problem, and formulate it into an integer linear programming with objectives of minimizing the embedding cost and the controller-to-switch delay for each vSDN. Moreover, we propose a novel online vSDN embedding algorithm called CO-vSDNE, which consists of a node mapping stage and a link mapping stage. In the node mapping stage, CO-vSDNE maps the controller and the virtual nodes to the substrate nodes on the basis of the controller-to-switch delay and takes into account the subsequent link mapping at the same time. In the link mapping stage, CO-vSDNE adopts the k-shortest path algorithm to map the virtual links. The evaluation results with simulation and Mininet emulation show that the proposed CO-vSDNE not only significantly increases the long-term revenue to the cost ratio and acceptance ratio while guaranteeing low average and maximum controller-to-switch delay, but also achieves good vSDN performance in terms of end-to-end delay and throughput.     

Optimal virtual network embedding: Energy aware formulation

Network Virtualization is a key component of the Future Internet, providing the dynamic support of different networks with different paradigms and mechanisms in the same physical infrastructure. A major challenge in the dynamic provision of virtual networks is the embedding approach taking energy efficiency into account, while not affecting the overall Virtual Network (VN) acceptance ratio. Previous research focused on either designing heuristic-based algorithms to address the efficient embedding problem or to address the energy impact.This paper proposes an integer linear programming formulation, Energy Aware–Virtual Network Embedding–Node-Link Formulation (EA–VNE–NLF), that solves the online virtual network embedding as an optimization problem, striving for the minimum energy consumption and optimal resource allocation per VN mapping. Two different objective functions are proposed: (i) addressing primarily the resource consumption problem – Bandwidth Consumption Minimization (BCM); (ii) addressing primarily the energy consumption problem – Energy Consumption Minimization (ECM).The performance of each objective function is evaluated by means of simulation and compared with an existing objective function, Weighted Shortest Distance Path (WSDP), that is considered state of the art of the resource allocation problem. The simulation results show that the objective function BCM reduces the energy consumption of the physical network by 14.4%, and improves the embedding factor by 4.3%, consuming almost the same amount of resources as requested, and slightly worsening the VN acceptance ratio by 2.3%. ECM reduces the energy consumption of the physical network by 31.4% and improves the embedding factor by 4.1%, without affecting the VN acceptance ratio when compared to WSDP.     

LIVE: Learning and Inference for Virtual Network Embedding

Network virtualization provides a promising tool for next-generation network management by allowing multiple heterogeneous virtual networks to run on a shared substrate network. A long-standing challenge in network virtualization is how to effectively map these virtual networks onto the shared substrate network, known as the virtual network embedding (VNE) problem. Most heuristic VNE algorithms find practical solutions by leveraging a greedy matching strategy in node mapping. However, greedy node mapping may lead to unnecessary bandwidth consumption and increased network fragmentation because it ignores the relationships between the mapped virtual network requests and the mapping ones. In this paper, we re-visit the VNE problem from a statistical perspective and explore the potential dependencies between every two substrate nodes. We define a well-designed dependency matrix that represents the importance of substrate nodes and the topological relationships between them, i.e., every substrate node’s degree of belief. Based on the dependency matrix generated from collecting and processing records of accepted virtual network requests, Bayesian inference is leveraged to iteratively select the most suitable substrate nodes and realize our novel statistical VNE algorithm consisting of a learning stage and an inference stage in node mapping. Due to the overall consideration of the relationships between the mapped nodes and the mapping ones, our statistical approach reduces unnecessary bandwidth consumption and achieves a better performance of embedding. Extensive simulations demonstrate that our algorithm significantly improves the long-term average revenue, acceptance ratio, and revenue/cost ratio compared to previous algorithms.     

Performance evaluation of artificial intelligence algorithms for virtual network embedding

Network virtualization is not only regarded as a promising technology to create an ecosystem for cloud computing applications, but also considered a promising technology for the future Internet. One of the most important issues in network virtualization is the virtual network embedding (VNE) problem, which deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in an offline situation. Some Artificial Intelligence (AI) techniques have been applied to the VNE algorithm design and displayed their abilities. This paper aims to compare the computational effectiveness and efficiency of different AI techniques for handling the cost-aware VNE problem. We first propose two kinds of VNE algorithms, based on Ant Colony Optimization and genetic algorithm. Then we carry out extensive simulations to compare the proposed VNE algorithms with the existing AI-based VNE algorithms in terms of the VN Acceptance Ratio, the long-term revenue of the service provider, and the VN embedding cost.     

Efficient Online Virtual Network Mapping Using Resource Evaluation

Network virtualization is a promising solution that can prevent network ossification by allowing multiple heterogeneous virtual networks (VNs) to cohabit on a shared substrate network. It provides flexibility and promotes diversity. A key issue that needs to be addressed in network virtualization is allocation of substrate resources for the VNs with respect to their resource requirements and the topologies of the substrate and virtual networks, namely the VN mapping (VNM) problem. Efficient VNM algorithms aim to maximize the number of coexisting VNs, and increase the utilization and revenue obtained from the substrate resources. In this paper, we present an online VNM algorithm (OVNM) that maximizes the number of coexisting VNs leading to good utilization and revenue of the substrate. Using the OVNM algorithm, we estimate the VN mapping and evaluate the associated substrate resources to map the VN within a proper region on the substrate by using the FVN_Sort (first virtual node sorting) function. This improves the probability of a VN mapping success. Furthermore, by mapping the virtual nodes and links in a coordinated fashion, the resource consumption while mapping is minimized. We evaluate the performance of our approach by using simulation, and show that the algorithm has an acceptable run time and leads to a better blocking probability performance, which means more coexisting VNs.     

A forwarding graph embedding algorithm exploiting regional topology information

Network function virtualization (NFV) is a newly proposed technique designed to construct and manage network functions dynamically and efficiently. Allocating physical resources to the virtual network function forwarding graph is a critical issue in NFV. We formulate the forwarding graph embedding (FGE) problem as a binary integer programming problem, which aims to increase the revenue and decrease the cost to a service provider (SP) while considering limited network resources and the requirements of virtual functions. We then design a novel regional resource clustering metric to quantify the embedding potential of each substrate node and propose a topology-aware FGE algorithm called ‘regional resource clustering FGE’ (RRC-FGE). After implementing our algorithms in C++, simulation results showed that the total revenue was increased by more than 50 units and the acceptance ratio by more than 15%, and the cost of the service provider was decreased by more than 60 units.     

Virtual network mapping algorithm in the cloud infrastructure

Network virtualization has received considerable attention recently because a Cloud Provider (CP) that is responsible for deploying a substrate network in the cloud infrastructure uses network virtualization to support multiple Virtual Network (VN) requests over the shared substrate network. However, mapping multiple VN requests with constraints on virtual nodes and virtual links into a shared substrate network presents a significant challenge, and is considered an NP-hard problem. In this paper, we propose a heuristic mapping algorithm that handles online VN requests. The node mapping algorithm selects a substrate node for mapping that satisfies both a virtual node's resource requirement and its amount of requested bandwidth. The link mapping algorithm either maps a virtual link to the shortest substrate path that satisfies the requested bandwidth of the virtual link or uses the cut-shortest path approach to map a virtual link to multiple substrate paths that satisfy the requested bandwidth of the virtual link. The path migration algorithm migrates virtual links to different substrate paths to maximize the number of accepted VN requests in a substrate network. Simulation results show that the proposed heuristic mapping algorithm uses resources more efficiently, produces more revenue, and has better performance than existing mapping approaches.     

一种基于协作博弈的虚拟网络嵌入策略

针对现有云服务中虚拟网络嵌入方法无法有效处理硬件故障的不足,提出一种基于协作博弈的高可靠性虚拟网络嵌入策略CG-VNE（virtual network embedding strategy based on cooperative game）,其目标是通过使客户们的接受率最大化使云供应方的收入最大,同时将底层路由器或链路故障导致的虚拟网络中断率降到最低．为了回避虚拟网络映射过程的指数级复杂度,CG-VNE将虚拟网络嵌入问题阐述为两个互相交错的协作博弈：第1个博弈处理虚拟节点映射问题,第2个博弈处理虚拟链路的嵌入问题．通过这两种博弈,虚拟博弈方通过合作即可达到纳什平衡,在提升云提供商的收入的同时有效地处理了路由器和链路的物理故障．全面的仿真实验结果表明,在新客户拒绝率、云服务收入及受到物理故障影响的客户率3个方面,相比于目前大多数虚拟网络嵌入算法而言,CG-VNE的性能提升明显．     

Virtual network embedding for power savings of servers and switches in elastic data center networks弹性数据中心网络中的服务器和交换机联合节能虚拟网络嵌入机制

Currently, the elastic interconnection has realized the high-rate data transmission among data centers (DCs). Thus, the elastic data center network (EDCN) emerged. In EDCNs, it is essential to achieve the virtual network (VN) embedding, which includes two main components: VM (virtual machine) mapping and VL (virtual link) mapping. In VM mapping, we allocate appropriate servers to hold VMs. While for VL mapping, an optimal substrate path is determined for each virtual lightpath. For the VN embedding in EDCNs, the power efficiency is a significant concern, and some solutions were proposed through sleeping light-duty servers. However, the increasing communication traffic between VMs leads to a serious energy dissipation problem, since it also consumes a great amount of energy on switches even utilizing the energy-efficient optical transmission technique. In this paper, considering load balancing and power-efficient VN embedding, we formulate the problem and design a novel heuristic for EDCNs, with the objective to achieve the power savings of servers and switches. In our solution, VMs are mapped into a single DC or multiple DCs with the short distance between each other, and the servers in the same cluster or adjacent clusters are preferred to hold VMs. Such that, a large amount of servers and switches will become vacant and can go into sleep mode. Simulation results demonstrate that our method performs well in terms of power savings and load balancing. Compared with benchmarks, the improvement ratio of power efficiency is 5%–13%.     

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.     

基于熵权VIKOR的安全虚拟网络映射算法

近年来,虚拟网络映射技术作为网络虚拟化的关键技术,成为学术界与工业界研究的重点之一。针对安全虚拟网络映射中因节点安全感知不全面、匹配不合理导致的映射性能较低问题,文章提出了一种基于熵权折衷排序法(VIKOR)的安全虚拟网络映射算法。该算法首先将安全虚拟网络映射问题构建为混合整数线性规划模型,设计了节点安全优先度指标,实现了虚拟网络节点与底层网络节点安全联合感知;其次在映射过程中综合考虑节点资源属性、拓扑属性和安全属性,采用熵权VIKOR进行节点排序;最后按照节点排序结果依次进行映射,其中链路映射采用k最短路径算法。仿真结果表明,在满足节点各项约束的前提下,文章算法提高了虚拟网络映射成功率和收益开销比。     

A green energy-aware hybrid virtual network embedding approach

In the past few years, the concept of network virtualization has received significant attention from industry and research fora, as it represents a promising way to diversify existing networks and ensure the co-existence of heterogeneous network architectures on top of shared substrates. Virtual network embedding (VNE) is the process of dynamically mapping virtual resources (i.e. virtual nodes and links) onto physical substrate resources. VNE is the main resource allocation challenge in network virtualization and is considered as an NP-hard problem. Several centralized and distributed VNE approaches have been proposed, with the aim of satisfying different objectives ranging from QoS, to economical profit, and network survivability. More recently, emerging VNE approaches started investigating the optimization of new objectives such as energy-efficiency and networks’ security. In this work, we propose a green energy-aware hybrid VNE hybrid VN embedding approach that aims at achieving energy efficiency and resource consolidation, while minimizing CO₂ emissions resulting from VNs operation. This approach consists of a hierarchical virtual networking management architecture in which control and management nodes collaborate for the splitting and embedding of sub-VNs requests to the cleanest substrate resources (i.e. the resources deployed in a sector with the smallest CO₂ emission factor) available. Three different variants of our VNE algorithms, taking into consideration different resources’ selection criteria (i.e. energy source, request priority, and request location) are presented, and their performance is compared with two existing VNE algorithms based on centralized and distributed embedding approaches. The comparative performance analysis shows that our proposed approach enables a more efficient VN embedding in terms of: a reduced number of substrate resources needed, a faster request mapping time, as well as resource consolidation and reduced resource cost. Furthermore, it enables a reduction of the carbon footprint of the VNE operation, thus resulting in a more green and environmentally conscious approach to network virtualization.     

一种双网同步搜索的虚拟网络映射算法

针对虚拟网络映射中的资源分配问题,通过建立双网同步搜索映射模型,采用普里姆最小生成树算法思想,同步搜索虚拟网络中的待映射虚拟节点和物理网络中的可映射物理节点,将相邻的虚拟节点依次映射到邻接的物理节点上,协调完成节点及其邻接链路的映射操作,使虚拟网络映射具有拓扑一致性。仿真实验表明,提出的DS-VNM算法能有效地降低虚拟链路的映射路径长度,提高网络收益与网络代价比、虚拟网络请求接受率,获得了较好的资源分配性能。     

基于知识描述和遗传算法的跨域虚拟网络映射

网络虚拟化环境下的跨域虚拟网络映射是指当物理网络由多个自治域构成时,以最小化虚拟网络映射开销为目标,将虚拟网络请求恰当地划分为多个虚拟子网请求,并分别指派给相应自治域以完成映射。资源匹配和虚拟网络划分是跨域虚拟网络映射中的两个关键阶段。然而,现有的资源匹配算法无法支持精确的数值属性匹配,也无法满足虚拟网络用户对表达多样化映射约束的需求,故实用性不高。此外,虚拟网络划分属于NP问题,目前也缺乏高效的求解方法。针对上述两个阶段中存在的问题,分别提出了基于OWL及SWRL的资源匹配算法和基于遗传算法的虚拟网络划分算法。理论分析证明了该方法的正确性。仿真实验从效率、性能及稳定性方面验证了该方法的有效性。     

主动休眠节点链路的高效节能虚拟网络映射

网络虚拟化,使得智能能量感知网络部署成为可能.由于当前网络为高峰负荷而设计,导致资源利用率不足及能量浪费.而网络设备能量消耗对于流量负载不敏感,资源整合成为有效节能技术.根据虚拟网络映射特点及底层网络能耗,提出虚拟网络映射节能多目标决策模型;由于该模型是混合整数规划模型,求解时间复杂度高,通过分析虚拟网络映射动态特征,构造虚拟网络映射字典库,提出底层网络资源利用率的训练方法以及主动休眠底层节点和链路算法,把虚拟网络映射在一个较小的节点和链路集合中,提高休眠节点和链路数量,实现高效节能虚拟网络映射.系统仿真结果验证了主动休眠方法能够提高底层节点和链路休眠数量,显著减少系统能耗.     

Topology awareness algorithm for virtual network mapping

Network virtualization is recognized as an effective way to overcome the ossification of the Internet. However, the virtual network mapping problem (VNMP) is a critical challenge, focusing on how to map the virtual networks to the substrate network with efficient utilization of infrastructure resources. The problem can be divided into two phases: node mapping phase and link mapping phase. In the node mapping phase, the existing algorithms usually map those virtual nodes with a complete greedy strategy, without considering the topology among these virtual nodes, resulting in too long substrate paths (with multiple hops). Addressing this problem, we propose a topology awareness mapping algorithm, which considers the topology among these virtual nodes. In the link mapping phase, the new algorithm adopts the k-shortest path algorithm. Simulation results show that the new algorithm greatly increases the long-term average revenue, the acceptance ratio, and the long-term revenue-to-cost ratio (R/C).     

网络虚拟化环境中虚拟网络的嵌套映射算法

网络虚拟化技术可以在共用的底层网络基础设施上同时构建多个彼此隔离的虚拟网络,为用户提供差异化服务,从而解决现有因特网的僵化问题.然而,一个重要的挑战是,如何在共用的基础设施中高效地映射多个具有不同拓扑的虚拟网络,即虚拟网络的嵌套映射问题.主要根据基础设施的构成方式对现有的虚拟网络映射算法进行了综述.首先,阐述了网络虚拟化的概念、特点以及相应的虚拟网络映射模型;其次,按照基础设施的构成方式、问题空间完整性、映射阶段数等方面梳理了嵌套映射算法的最新研究进展;最后,对虚拟网络映射算法在公平性、扩展性、高利用率、信任度等未来可能的发展方向进行了展望.     

EE-CTA: Energy efficient,concurrent and topology-aware virtual network embedding as a multi-objective optimization problem

Network virtualization provides the substruction of various heterogeneous Virtual Networks (VNs) on a single physical infrastructure which is called Virtual Network Embedding (VNE) and is known as an Np-hard problem. The VNE includes two sub-problem; virtual node mapping and virtual link mapping. Related works do not consider network topology and energy efficiency in the embedding process. This paper proposes Energy Efficient, Concurrent, and Topology-Aware (EE-CTA) algorithm as a new concurrent VNE method. Also, EE-CTA is energy-efficient due to using servers status and renewable energy resources when they are available. Our proposed EE-CTA has focused on network topology with assigning reachability rank to resources. In order to achieve all of these goals, we model VNE as a multi-objective optimization problem and solve it by Non-dominated Sorting Genetic Algorithm (NSGA-II). We compare EE-CTA with Presto, Ant Colony Optimization (ACO), Topology and Migration-Aware Energy Efficient (TMAE), and RW-Max match methods. The evaluation results demonstrate our method improves revenue, acceptance ratio, cost, and energy usage.     

A novel paths algebra-based strategy to flexibly solve the link mapping stage of VNE problems

One of the main challenges of network virtualization is the virtual network embedding problem (VNE) that consists of mapping virtual network demands in physical network resources. VNE can be decomposed into two stages: virtual node and virtual link mapping. In the first stage, each virtual node is mapped to a suitable node in the physical network whereas the second stage is in charge of mapping the links connecting virtual nodes to paths in the physical network that suit the virtual network demands.In this paper we propose the utilization of a mathematical multi-constraint routing framework called “paths algebra” to solve the virtual link mapping stage. Paths algebra provides the flexibility to introduce an unlimited number of linear and non-linear constraints and metrics to the problem while finding all the eligible paths in the physical network to perform the virtual link mapping resulting in better and more flexible embeddings.     

基于混合群智能优化的虚拟网络映射算法

网络虚拟化是突破网络发展僵局的一项重要技术,而虚拟网络映射(VNE)是网络虚拟化的一个主要问题。提高底层网络资源的利用率和收益是虚拟网络映射的主要目标。针对底层网络支持路径分裂的情况,建立了整数线性规划(ILP)模型,并提出基于混合群智能优化的虚拟网络映射算法。该算法在兼顾映射开销和映射均衡性的基础上利用粒子群优化算法(PSO)和遗传算法(GA)迭代优化映射方案。仿真实验结果表明,与现有的主流研究成果相比,该算法显著地提高了底层网络长期平均运营收益与虚拟网络请求接受率。