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.     

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.     

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.     

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.     

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.     

考虑节能的拓扑聚合虚拟网映射算法

网络虚拟化的关键问题是虚拟网映射,能耗开销的快速增长使得节能成为底层设施供应商关注的目标。针对虚拟网映射中的节能问题,提出一种集中使用网络拓扑的节能虚拟网映射算法。该算法引入接近度中心度概念和节点能力共同表征节点的重要程度,优先使用已工作节点进行资源整合使用,同时通过检验保证底层链路距离不会过长,有利于减少能耗和开销。实验仿真结果表明该算法在接受率达到70%、长期收益开销比达到75%的同时,使收益能耗比提高20%以上,与之前算法相比具有优势。     

基于节点连通性排序的虚拟网络映射算法

对当今云环境下的数据中心来说,以虚拟资源租赁的运营方式具有极大的灵活性,尤其是以虚拟网络为粒度的资源租赁能够为用户提供更好的个性化需求支持。虚拟网络映射问题是指依据用户资源需求,合理分配底层主机和网络资源。现有的虚拟网络映射算法大多是针对随机拓扑设计的通用算法,未针对数据中心拓扑结构进行优化,映射效率有很大提升空间。针对数据中心的结构特点,提出了一种基于节点连通性排序的虚拟网络映射算法BS-VNE算法。首先,设计了一种最大生成算法来对虚拟节点重要程度进行求解和排序。该算法不仅基于虚拟节点的带宽和连通度,还基于虚拟节点在整个虚拟网络中的连通性来进行节点连通性的计算,以获得更加合理的排序结果。然后,根据虚拟节点连通性排序结果利用离散粒子群优化算法求解虚拟网络的映射解。在求解过程中,引入了针对数据中心结构的物理网络拓扑启发式规则,并将其组合到粒子搜索过程中,以提高映射算法的收敛速度。仿真实验结果表明,与现有算法相比,本文提出的算法可以提高物理网络的收益/成本比和资源利用率。     

An evaluation method for secure virtual network embedding algorithms

Due to the ossification of the current Internet, it is difficult to launch new service. One of solutions is network virtualization. Numerous virtual network embedding (VNE) algorithms have been proposed in many literatures. But, there is no general methods or frameworks for evaluation of these algorithms. We have analyzed a number of studies, and found appropriate evaluation indexes that can be used in evaluating the functionalities of VNE algorithms. Based on those indexes, we presented a new evaluation method of secure VNE algorithms. To make a virtual network with the various requirements, the infrastructure provider needs effective resource allocation algorithm. The role of VNE is to allocate physical resources to virtual nodes or links to form virtual networks. In order to use the resources of physical networks, appropriate resource allocation algorithms are required. We found a set of evaluation indexes by analyzing the previous proposed researches. Through analysis, we found that our proposed method can be grouped into two functional attributes for classification. One attribute is the basic attributes that mean special features of architecture and the other is the evaluation attributes that perform the assessment of algorithms. We have evaluated the algorithms with our proposed evaluation methods and found to be useful to choose the appropriate algorithm to the infrastructure provider. The proposed method was found to be more convenient to perform the evaluation of the algorithms in real-world simulation. This method helps infrastructure providers to choose the appropriate VNE algorithm.     

基于离散粒子群的节点可重用虚拟网络映射算法

虚拟网络映射问题是网络虚拟化要解决的重点问题,也是云计算环境下实现资源多租赁运营的技术基础。现有的映射算法在计算效率上有待提高,不能充分利用可重用技术以节省网络带宽资源。提出一种可重用的虚拟网络映射算法,首先构建以提高底层物理网络利用率为目标的资源优化分配模型;然后再充分利用可重用技术以内存交换替代网络交换并针对效率问题设计增强的粒子初始位置分配算法,进而通过离散粒子群算法对优化问题进行求解。仿真实验结果表明,提出的算法相较已有的普通粒子群算法在物理网络收益上有显著提高,增强的初始位置分配机制也有助于计算效率的提升。     

Greener networking in a network virtualization environment

Energy consumption of network operators can be minimized by the dynamic and smart relocation of networking resources. In this paper, we propose to take advantage of network virtualization to enable a smart energy aware network provisioning. The virtualization of networking resources leads to the problem of mapping virtual demands to physical resources, known as Virtual Network Embedding (VNE). Our proposal modifies and improves exact existing energy aware VNE proposals where the objective is to switch off as many network nodes and interfaces as possible by allocating the virtual demands to a consolidated subset of active physical networking equipment. As exact energy efficient VNE approaches are hard to solve for large network sizes and have an adverse effect in the number of successful embeddings, an heuristic approach to reconfigure the allocation of already embedded virtual networks, minimizing the energy consumption, is also proposed.     

Embedding of virtual network requests over static wireless multihop networks

Network virtualization is a technology of running multiple heterogeneous network architecture on a shared substrate network. One of the crucial components in network virtualization is virtual network embedding, which provides a way to allocate physical network resources (e.g., CPU and link bandwidth) to virtual network requests. Despite significant research efforts on virtual network embedding in wired and cellular networks, little attention has been paid to that in wireless multi-hop networks, which is becoming more important due to its rapid growth and the need to share these networks among different business sectors and users. In this paper, we first study the root causes of new challenges of virtual network embedding in wireless multi-hop networks, and propose a new embedding algorithm that efficiently uses the resources of the physical substrate network. We examine our algorithm’s performance through extensive simulations under various scenarios. Due to lack of competitive algorithms, we compare the proposed algorithm to five other algorithms, mainly borrowed from wired embedding or made by us, partially with or without the key algorithmic ideas to assess their impacts.     

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.     

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.     

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.     

虚拟网络映射模型及其优化算法

网络虚拟化被视为构建新一代互联网体系架构的重要技术,它使得能在一个共享的底层物理网络上同时运行多个网络架构或网络应用,从而能为用户提供多样化的端到端定制服务.虚拟网络映射是实现网络虚拟化的关键环节,其目的是在满足虚拟网络资源需求的前提下,将虚拟网络植入到合适的底层物理节点和链路.虚拟网络映射需要解决资源约束、准入控制、在线请求和拓扑多样性等多方面的问题.根据应用场景、优化目标、映射方式和约束条件的不同,可以得到不同类型的虚拟网络映射优化问题.这些优化问题通常是NP难的.通过形式化建立了虚拟网络映射模型,归纳了虚拟网络映射的方法和算法.总结了解决虚拟网络映射模型优化问题的几条技术途径,指出了该领域中需要进一步研究的热点问题.     

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).     

高效节能虚拟网络映射多反馈控制模型及算法

网络虚拟化使得智能能耗感知网络部署成为可能.由于虚拟网络请求到来以及退出等动态性,引起底层网络资源分配以及回收,会对底层网络激活资源数量与集合范围产生影响;虚拟网络映射不仅决定了当前激活的底层网络资源数量与集合大小,而且作用于后续的虚拟网络映射.本文利用自动控制原理的反馈控制理论,研究不同虚拟网络映射之间的关系以及当前虚拟网络映射对激活底层网络资源集合产生的影响,并提出一种新的高效节能虚拟网络映射多反馈控制模型及算法.以控制底层网络休眠链路数量作为主反馈,消除主动休眠的底层网络链路数量与被动休眠的链路数量的偏差,抑制虚拟网络映射动态特征对求解最小底层网络激活资源集合的干扰;并以节点和链路映射为局部反馈,由大到小逐步调整主动休眠底层链路数量,快速地找到适合当前虚拟网络请求的最小底层网络资源集合.多反馈控制模型能够把虚拟网络映射在一个较小的节点和链路集合中,从而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在非饱和状态下虚拟网络映射多反馈控制算法能够提高底层节点和链路休眠数量,显著减少系统能耗;且在负载周期性动态变化的饱和状态下,提高了虚拟网络接收率以及系统收益.     

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。     

虚拟网映射问题的计算复杂性分析

虚拟网映射是实现网络虚拟化的关键环节,其任务是在满足虚拟网构建约束的前提下,把虚拟网的虚拟节点和虚拟链路分别映射到底层物理网的节点和路径上。文中根据虚拟节点映射是否已知、物理网是否支持路径分割、物理节点是否支持重复映射等特征,对虚拟网映射问题进行分类,并针对一般网络拓扑模型和某些特殊网络拓扑模型完成各类虚拟网映射可行问题和优化问题的计算复杂性分析。     

一种基于约束优化的虚拟网络映射方法

虚拟网络映射问题将不同的虚拟网络应用映射到相同的基础设施网络中,这是一个极具挑战性的问题.针对该问题,提出了一种基于约束优化的虚拟网络映射方法,将映射问题分解为节点映射和链路映射两个阶段,其中,前者是将虚拟节点映射到物理节点上,后者将虚拟链路映射到物理路径上,它们都是NP难问题.针对节点映射和链路映射分别提出了node-mapping算法和link-mapping算法.node-mapping算法基于贪婪算法的思想,映射时考虑了物理节点所能提供的资源数量以及物理节点间距离两个因素,该算法能够保证基础设施网络中各节点间的负载相对均衡;同时,通过采用访问控制机制,过滤一些异常的虚拟网络请求,能够有效地提高资源的使用效率.link-mapping算法基于人工智能领域中的分布式约束优化思想,其能够保证得到的解是全局最优的,即映射链路的代价最小.最后,通过模拟实验对该方法进行验证,实验结果表明该方法在求解虚拟网络映射问题时的性能良好.