COVE: Co-operative Virtual Network Embedding for Network Virtualization

Network virtualization provides a promising solution for next-generation network management by allowing multiple isolated and heterogeneous virtual networks to coexist and run on a shared substrate network. A long-standing challenge in network virtualization is how to effectively and efficiently map these virtual nodes and links of heterogeneous virtual networks onto specific nodes and links of the shared substrate network, known as the Virtual Network Embedding (VNE) problem. Existing centralized VNE algorithms and distributed VNE algorithms both have advantages and disadvantages. In this paper, a novel cooperative VNE algorithm is proposed to coordinate centralized and distributed algorithms and unite their respective advantages and specialties. By leveraging the learning technology and topology decomposition, autonomous substrate nodes entrusted with detailed mapping solutions cooperate closely with the central controller with a global view and in charge of general management to achieve a successful embedding process. Besides a topology-aware resource evaluation mechanism and customized mapping management policies, Bloom filter is elaborately introduced to synchronize the mapping information within the substrate network, instead of flooding which generates massive communication overhead. Extensive simulations demonstrate that the proposed cooperative algorithm has acceptable and even better performance in terms of long-term average revenue and acceptance ratio than 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.     

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.     

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。     

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。     

基于负载均衡的无线虚拟网络映射算法

针对网络僵化的问题,目前多采用网络虚拟化（NV）方法进行解决,其关键技术是虚拟网络映射（VNE）。为解决无线VNE过程中功率和带宽资源使用不均衡的问题,基于负载均衡原理提出一种联合资源分级的无线VNE算法。首先,采用新的节点资源排序方式,其中将节点功率和平均链路带宽作为排序依据;其次,对资源进行分级,以动态调整虚拟网络请求对功率和带宽资源的需求;最后,改进功率和带宽资源的单位成本,并以最小化成本为目标函数选择资源分配方案。与原有的无线VNE算法WVNE-JBP相比,所提算法的总体接受率提高了11.7个百分点,平均功率利用率提高了4.4个百分点,平均带宽利用率提高了1.6个百分点。实验结果表明,所提算法能有效提高虚拟网络接受率和资源利用率。     

Operations Research Methods for Autonomic Resource Management

Varying service demands on next generation networks requires autonomic resource management capabilities to ensure service delivery to customers, motivating the need to develop self-managing algorithms for this purpose. In this paper, we use algorithms from the Operations Research community for this purpose. First, we adapt the Transportation Model to manage distributed resources in the next generation network infrastructure. Coupled with monitoring capabilities, the proposed scheme can automatically adjust the virtual resource allocation to optimize the costs incurred by service providers in offering services to customers. Second, we propose the use of Inventory Control to predict needs for virtual resources and to pre-order required virtual resource amounts. We compare two possible Inventory Control models to manage the virtual resources involved in service delivery. We perform extensive simulations to show the performance improvements made possible by the use of the Transportation Model and Inventory Control for autonomic resource management in next generation networks.     

虚拟时钟调度算法研究

FIFO队列调度算法由于实现简单、执行效率高而在网络中得到大量的应用，但FIFO队列无法实现对资源的分配；男种调度算法公平队列调度则可以实现对带宽资源的公平分配，但是它存在可扩展性问题。而基于虚拟时钟的算法在实现对资源进行公平分配的同时具备良好的可扩展性，从而能够满足QoS控制中对于分组调度算法的需求。本文主要对一些基于虚拟时钟的度算法进行了分析和讨论，这也是进行QoS控制研究的基础。     

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.     

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.     

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.     

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.     

基于虚电路的拒绝服务攻击防御研究*

设计了一种基于虚电路的拒绝服务保护基体系结构;介绍了基于虚电路的资源分配算法;在基于服务元网络体系结构的虚电路结构原型系统中实现了所提出的资源分配算法。与其他算法相比,该算法能有效对抗来自网络的恶意授权实体的拒绝服务攻击。     

基于拓扑分割与聚类分析的虚拟软件定义网络映射算法

针对目前大部分基于虚拟软件定义网络（vSDN）的映射算法未充分考虑节点与链路之间的相关性的问题,提出了一种基于网络拓扑分割与聚类分析的vSDN映射算法。首先,通过根据最短跳数进行拓扑分割的方法,降低物理网络的复杂度;然后,通过根据节点拓扑和资源属性进行聚类分析的方法,提升映射算法的请求接受率;最后,通过将链路约束分散到节点带宽资源以及节点的度进行约束考量,对不符合链路要求的节点进行重映射,从而优化了节点与链路映射过程。实验结果表明,该算法有效地提升了基于软件定义网络（SDN）架构的虚拟网络映射算法在较低连通概率物理网络下的请求接受率。     

Performance analysis based resource allocation for green cloud computing

Cloud computing has become a new computing paradigm that has huge potentials in enterprise and business. Green cloud computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. To maximize utilization and minimize total cost of the cloud computing infrastructure and running applications, resources need to be managed properly and virtual machines shall allocate proper host nodes to perform the computation. In this paper, we propose performance analysis based resource allocation scheme for the efficient allocation of virtual machines on the cloud infrastructure. We experimented the proposed resource allocation algorithm using CloudSim and its performance is compared with two other existing models.     

一种虚拟路由器资源映射算法研究

网络虚拟化技术的提出,为解决互联网"僵化"问题找到了新的思路,受到广泛的关注。在虚拟路由器平台中,若干台互联的网络服务器资源组成了底层物理网络,通过虚拟网络映射技术,将物理网络资源有效地映射到虚拟网络设备上,组成多个虚拟网络,满足用户对网络的多样化需求。虚拟路由器资源映射问题是虚拟网络映射问题的基础,虚拟路由器实例与物理资源的映射方法决定了虚拟网络平台资源的利用率和虚拟网络系统的性能。针对虚拟路由器平台资源分配的问题,提出了物理网络资源模型和虚拟路由器资源请求模型,设计了一种启发式虚拟路由资源分配算法,并对算法的复杂性和优化目标进行了分析。     

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。     

虚拟化云计算数据中心能量感知资源分配机制

针对云计算的资源管理问题,提出了云计算数据中心的能量模型以及四个虚拟机放置算法。首先计算每个机架上主机的负载并根据设定的阈值进行归类,然后采用最少迁移策略从主机上选择合适迁移的虚拟机并且接受新的虚拟机分配请求,对每个虚拟机与主机集合进行匹配,选择最优化的主机进行放置。实验结果表明,与现有的能量感知资源分配方法相比,该方法在主机、网络设备以及冷却系统方面能量利用率分别提高了2.4%,18.5%和28.1%,总的能量利用率平均提高了14.5%。     

Topology-aware virtual network embedding based on closeness centrality

Network virtualization aims to provide a way to overcome ossification of the Internet. However, making efficient use of substrate resources requires effective techniques for embedding virtual networks: mapping virtual nodes and virtual edges onto substrate networks. Previous research has presented several heuristic algorithms, which fail to consider that the attributes of the substrate topology and virtual networks affect the embedding process. In this paper, for the first time, we introduce complex network centrality analysis into the virtual network embedding, and propose virtual network embedding algorithms based on closeness centrality. Due to considering of the attributes of nodes and edges in the topology, our studies are more reasonable than existing work. In addition, with the guidance of topology quantitative evaluation, the proposed network embedding approach largely improves the network utilization efficiency and decreases the embedding complexity. We also investigate our algorithms on real network topologies (e.g., AT&T, DFN) and random network topologies. Experimental results demonstrate the usability and capability of the proposed approach.     

Multi-layer Virtual Transport Network Design

Service overlay networks and network virtualization enable multiple overlay/virtual networks to run over a common physical network infrastructure. They are widely used to overcome deficiencies of the Internet (e.g., resiliency, security and QoS guarantees). However, most overlay/virtual networks are used for routing/tunneling purposes, and not for providing scoped transport flows (involving all mechanisms such as error and flow control, resource allocation, etc.), which can allow better network resource allocation and utilization. Most importantly, the design of overlay/virtual networks is mostly single-layered, and lacks dynamic scope management, which is important for application and network management. In response to these limitations, we propose a multi-layer approach to virtual transport network (VTN) design. This design is a key part of VTN-based network management, where network management is done via managing various VTNs over different scopes (i.e., ranges of operation). We explain the details of the multi-layer VTN design problem as well as our design algorithms, and focus on leveraging the VTN structure to partition the network into smaller scopes for better network performance. Our simulation and experimental results show that our multi-layer approach to VTN design can achieve better performance compared to the traditional single-layer design used for overlay/virtual networks.