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.     

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.     

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.     

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.     

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.     

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.     

网络存储的统一与虚拟化

存储资源共享是网络存储的最基本的任务，为了给用户提供有效的存储资源共享，网络存储的统一化和虚拟化采取了不同的方法：统一化的侧重点在于存储技术、存储资源的整合和存储管理的研究，虚拟化侧重于如何为用户提供更方便的应用环境。统一虚拟存储体系结构则结合了两者的优点，代表着网络存储的将采。本文对网络存储的统一化和虚拟化的现状和存在问题进行了探讨，分析了阻碍统一虚拟存储体系结构实现的不利因素，针对这些存在的问题，本文给出了一个基于自主式存储中介的网络存储系统，该系统结合了网络存储的统一化和存储虚拟化技术，可以看作统一的虚拟存储网络的一个实现。     

基于OpenFlow的软件定义网络虚拟化方案

软件定义网络(SDN)可以将网络控制平面与数据平面分离开来,为网络虚拟化提供了良好的平台。为了解决SDN中多租户下的虚拟化,提出了一种基于OpenFlow的网络虚拟化方案。通过一个中间代理来转换并匹配物理MAC地址与虚拟MAC地址,以及物理流表项和虚拟流表项,以此实现流量空间的虚拟化。其中,根据实际数据包的惰性计算,使用前缀或通配符来精确匹配流表项。另外,为了保障物理OpenFlow网络上不同租户之间的隔离,将单个虚拟MAC-通配符流表项映射为多个具有精确MAC地址的物理流表项。实验结果表明,该方案成功的实现了网络虚拟化,且虚拟化开销较小,具有可行性。     

Energy aware virtual network embedding with dynamic demands: Online and offline

In Network as a Service model in cloud computing, how to efficiently embed virtual networks with both node and link demands into a shared physical network, namely virtual network embedding, has attracted significant attention. Most of prior studies on this problem have the following two limitations: (i) they assumed that the virtual network demands are constants, which does not hold in real-world network since such demands may vary a lot over time; (ii) their primary goal was to achieve more revenues for the physical network, with no consideration of the energy cost, which has become a more and more critical issue. In this paper, we bridge the gaps and study the energy aware virtual network embedding problem with dynamic demands. Specifically, we first model the dynamics of virtual network demands as a combination of a Gaussian distribution and a daily diurnal pattern. We then design two efficient heuristic algorithms by leveraging the dynamic characteristic of virtual network demands to minimize the energy consumption while keeping a high revenue for the physical network. One algorithm processes the virtual network requests one by one while the other one processes them group by group. We implemented these two algorithms in C++ and performed side-by-side comparisons with the prior algorithm. Extensive simulations show that our algorithms significantly reduce the energy cost by up to 25% over the state-of-the-art algorithm, while maintaining near the same revenue.     

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.     

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。     

异构云平台中能源有效的虚拟机部署研究

能源消耗已经成为数据中心操作成本的重要组成部分,虚拟化技术是降低数据中心能源消耗的有效方法之一.为了降低数据中心过高的能源消耗,利用虚拟化技术,结合数据中心中物理机的异构性和虚拟机所需资源的多维性,提出了一个衡量不同类型物理机性能的模型和一个衡量多维资源利用率的模型,在此基础上提出了一个异构云平台下能源有效的虚拟机部署算法.仿真实验表明,与MBFD算法及BFD算法相比,该算法不仅可以有效地降低系统的能源消耗,而且还提高了资源利用率,减少了资源的浪费.     

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。     

Novel methods for virtual network composition

Network virtualization has been proposed as a technology that aims to solve the Internet ossification. Central to the network virtualization is a virtual network composition mechanism providing an efficient mapping of virtual nodes and links onto appropriate physical resources in the network infrastructure.This paper proposes a novel backtracking heuristic algorithm for virtual network composition. Based on this algorithm, two approaches with two different objectives are presented. The first approach (Backtracking-CR) aims to compose a virtual network using the least amount of network resources, while the second (Backtracking-LB) applies load balancing for virtual network composition. Furthermore, a linear programming approach that optimizes the virtual network composition with an objective of using the least amount of network resources is presented and used to bench mark the heuristic algorithm. Simulation results show that using less network resources by applying linear programming or Backtracking-CR does not produce higher number of successfully mapped virtual networks when is compared to load balancing approach. Results also show that the proposed heuristic algorithm is scalable to large physical and virtual networks with respect to the computation time.     

Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems

Virtualization facilitates the provision of flexible resources and improves energy efficiency through the consolidation of virtualized servers into a smaller number of physical servers. As an increasingly essential component of the emerging cloud computing model, virtualized environments bill their users based on processor time or the number of virtual machine instances. However, accounting based only on the depreciation of server hardware is not sufficient because the cooling and energy costs for data centers will exceed the purchase costs for hardware. This paper suggests a model for estimating the energy consumption of each virtual machine without dedicated measurement hardware. Our model estimates the energy consumption of a virtual machine based on in-processor events generated by the virtual machine. Based on this estimation model, we also propose a virtual machine scheduling algorithm that can provide computing resources according to the energy budget of each virtual machine. The suggested schemes are implemented in the Xen virtualization system, and an evaluation shows that the suggested schemes estimate and provide energy consumption with errors of less than 5% of the total energy consumption.     

Opportunistic sharing scheme for spectrum allocation in wireless virtualization

The past few decades have witnessed an increasing growth in mobile and wireless network, leading to a corresponding fast growth in mobile demands. However, the proliferating mobile demands compel wireless network to face several challenges, such as the conflict between spectrum crisis and low resource utilization ratio, and the poor quality of service and quality of experience. Wireless virtualization, enabling multiple concurrent virtual networks running on shared wireless substrate resource, has been proposed as a promising way to overcome the plights of the current mobile and wireless network. How to efficiently allocate the resource, especially the spectrum resource, of physical network to multiple virtual networks is one fundamental and important challenge in wireless virtualization. This paper rethinks the characteristics of virtual networks’ requirements, and then divides the requirement into a baseline part and a fluctuant part. Based on it, this paper introduces an opportunistic spectrum sharing approach, through which we formulate the spectrum resource allocation problem as an NP-Hard problem. Then, we propose our opportunistic spectrum resource allocation scheme for the wireless virtualization. Simulations validate the performance advantages of our approach and show that opportunistic spectrum sharing significantly improves the revenue, resource utilization and acceptance ratio of physical wireless network while decreasing the payments of virtual networks.     

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。     

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。     

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。