Topology-aware virtual network embedding based on closeness centrality

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  • Front. Comput. Sci., 2013, 7(3): 446457

    DOI 10.1007/s11704-013-2108-4

    Topology-aware virtual network embedding based oncloseness centrality

    Zihou WANG 1, Yanni HAN1, Tao LIN1, Yuemei XU1, Song CI1,2, Hui TANG1

    1 High Performance Network Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China

    2 Department of Computer and Electronics Engineering, University of Nebraska-Lincoln, NE 68182, USA

    c Higher Education Press and Springer-Verlag Berlin Heidelberg 2013

    Abstract Network virtualization aims to provide a way toovercome ossification of the Internet. However, making e-

    cient use of substrate resources requires eective 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 net-

    works aect 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 topol-

    ogy, our studies are more reasonable than existing work. In

    addition, with the guidance of topology quantitative evalua-

    tion, the proposed network embedding approach largely im-

    proves the network utilization eciency 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 us-

    ability and capability of the proposed approach.

    Keywords network virtualization, virtual network embed-ding, complex networks, closeness centrality

    1 Introduction

    Solving the ossification problem of the Internet with network

    Received March 26, 2012; accepted July 31, 2012

    E-mail: wangzh@hpnl.ac.cn

    virtualization has received a lot of attention in the past few

    years [14]. The IP-based architecture makes the Internet

    successful for deploying heterogeneous networks. Neverthe-

    less, there are more and more emerging services on the In-

    ternet which have dierent requirements that the current IP-

    based Internet cannot meet. At the same time, because of the

    multi-ISP environment, large-scale network innovation and

    experiments cannot be tested. Many researchers have tried

    to solve the problem, and a number of solutions and testbeds

    have focused on network virtualization technologies [57].

    In network virtualization environment, the traditional role

    of the internet service provider (ISP) is divided into two roles:

    the infrastructure provider (InP) who maintains the substrate

    network, and the service provider (SP) who creates virtual

    networks. SPs rent the physical resources from InPs to create

    virtual networks and deploy end-to-end services to meet user

    requirements. The principal advantage of network virtualiza-

    tion is that multiple virtual networks will be able to coexist

    on the same substrate network and oer various customized

    services at the same time. For example, in network virtu-

    alization environment, online games and IPTV can perform

    simultaneously on dierent virtual networks without interfer-

    ence.

    Network virtualization research faces many challenges.

    For example, virtual resource description, virtual network in-

    stantiation, and virtual network management. The fundamen-

    tal problem in network virtualization is the virtual network

    embedding problem, i.e., eectively mapping the virtual net-

    work requests to the substrate network with the minimum

    cost of physical resources. Due to multiple objectives and

  • Zihou WANG et al. Topology-aware virtual network embedding based on closeness centrality 447

    multiple constraints, the virtual network embedding problem

    turns out to be NP-hard [3], and several heuristic algorithms

    have been proposed in recent years [816]. Most of them

    map the virtual networks in two independent phases. In the

    first phase, the virtual nodes are mapped with greedy meth-

    ods ignoring topology attributes. Then in the second phase,

    the edges are mapped with shortest path-based algorithms.

    However, separately considering the node and edge map-

    ping processes will restrict the solution space, and lead to de-

    creased utilization of substrate network resources and a lower

    revenue of the InP. In this paper, we jointly consider the two

    node and edge mapping phases by measuring the significance

    of the nodes in the global network topology when mapping

    the nodes in the first phase. And for the first time, we in-

    troduce network centrality analysis into the virtual network

    embedding problem. With the guidance of centrality anal-

    ysis, we develop two novel algorithms to achieve eective

    resource utilization.

    In a network, even nodes with the same available re-

    sources, vary in their importance due to their dierent loca-

    tions. It is reasonable to firstly choose the substrate nodes

    with the same available resources in a more important loca-

    tion. Furthermore, the importance of a node is more com-

    plex when the network is dynamically changing. The current

    states of all the elements in the global network determine the

    importance of a node. Centrality analysis provides eective

    methods for measuring the importance of nodes in a com-

    plex network and it has been widely used in complex network

    analysis, especially in social network analysis. In the scope

    of centrality analysis, the nodes can be characterized by mul-

    tidimensional measures [17, 18].

    To the best of our knowledge, no existing literature has ex-

    plored the relationship between network centrality analysis

    and the virtual network embedding problem. We map vir-

    tual nodes using a fast selective algorithm based on the mea-

    surement of nodes by network centrality. Dierent from ex-

    isting solutions which map the nodes only considering local

    resources, e.g., CPU and bandwidth of the adjacent edges,

    we analyze the characteristics of network topology from a

    global view, and take into consideration the topology prop-

    erty in computing the resource availability, rather than only

    resources of the nodes. The key advantage of this method is

    that the virtual nodes are mapped to the more important sub-

    strate nodes in a preferential manner. The importance of a

    node is jointly determined by its own resources and location

    in the entire topology.

    The major contributions in this paper are summarized in

    the following:

    Introducing network centrality to the virtual networkembedding problem. When embedding the virtual net-

    works, sorting the nodes with the topology-aware close-

    ness centrality method from network centrality analysis.

    Extending the closeness centrality to a new formatwhich is more appropriate for the virtual network em-

    bedding problem. The classical definition of closeness

    only considers the topology. Inspired by field theory, we

    redefine closeness, which consider topology attributes

    with the dynamic states of the nodes and edges at the

    same time.

    Evaluating the proposed algorithms based on networkcentrality. Our results show that, centrality based algo-

    rithms achieve better performance. The acceptance ratio

    of the two proposed algorithms is much higher than the

    benchmark algorithm. The improved algorithm also de-

    creases the cost of the substrate network.

    In Section 2, the network model and the VN embedding

    problem are formally defined. In Section 3, we introduce

    network centrality to analyze the topologies of substrate net-

    works and virtual networks. Two VN embedding algorithms

    based on closeness are proposed in Section 4 and Section 5.

    We evaluate the algorithms using experiments in Section 6.

    In Section 7, we briefly review the related work. The paper

    concludes in Section 8.

    2 Virtual network embedding problem

    In this section, we describe the general virtual network em-

    bedding problem.

    2.1 Network model

    The substrate network topology of the InP is modeled as a

    weighted graph, GS = (NS , ES ), where NS refers to the set

    of nodes of the substrate network, while ES refers to the

    set of edges of the substrate network. Each substrate node

    nS NS is associated with an available CPU capacity valuec(nS ), while each substrate edge eS (i, j) ES between nodesni and n j is associated with an available bandwidth capacity

    value bw(eS ).

    The virtual networks of the SP are defined similarly. The

    ith arriving virtual network request is denoted by GiV =

    (NiV , EiV ), where N

    iV and E

    iV refer to the sets of virtual nodes

    and virtual edges of the ith arriving virtual network request,

    respectively. Each virtual node nV NiV is associated with aCPU requirement value c(nV) and each virtual edge eV (i, j) EiV between nodes ni and n j is associated with a bandwidth re-

  • 448 Front. Comput. Sci., 2013, 7(3): 446457

    quirement bw(eV). For each request, it has a lifetime td(GiV ).

    If the VN request has been embedded on the substrate net-

    work, the allocated resources will be dedicated to the VN

    during its lifetime. When the lifetime of the VN is over