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  • VIRTUAL NETWORK EMBEDDING FOR SOFTWARE DEFINED

    NETWORKS

    Ljiljana Trajkovi

    Communication Networks Laboratory http://www.ensc.sfu.ca/~ljilja/cnl/

    Simon Fraser University Vancouver, British Columbia, Canada

  • Communication Networks Laboratory

    Ph.D. student: Soroush Haeri

    October 6, 2015 IWCSN 2015, Perth, Australia 2

  • Roadmap

    Network virtualization Virtual network embedding (VNE):

    objective R-Vine and D-Vine algorithms Global Resource Capacity algorithm

    Virtual network embedding, software defined networks, and data centers

    Data center topologies Simulation results Conclusions and References

    October 6, 2015 IWCSN 2015, Perth, Australia 3

  • Network virtualization

    Enables coexistence of multiple virtual networks on a physical infrastructure

    Virtualized network model divides the role of Internet Service Providers (ISPs) into: Infrastructure Providers (InPs)

    manage the physical infrastructure Service Providers (SPs)

    aggregate resources from multiple InP into multiple Virtual Networks (VNs)

    October 6, 2015 IWCSN 2015, Perth, Australia 4

  • Substrate network vs. virtual network

    InPs operate physical substrate networks (SNs) SN components:

    physical nodes (substrate nodes) physical links (substrate links)

    Substrate nodes and links are: interconnected using arbitrary topology used to host various virtualized networks with

    arbitrary topologies Virtual networks are embedded into a substrate network

    October 6, 2015 IWCSN 2015, Perth, Australia 5

  • Virtual network embedding

    Virtual Network Embedding (VNE) allocates SN resources to VNs

    InPs revenue depends on VNE efficiency VNE problem may be reduced to a mixed-integer

    programming: NP-hard problem optimal solution may only be obtained for small

    instances of the problem

    October 6, 2015 IWCSN 2015, Perth, Australia

    M. Yu, Y. Yi, J. Rexford, and M. Chiang, Rethinking virtual network embedding: substrate support for path splitting and migration,

    Comput. Commun. Rev., vol. 38, no. 2, pp. 1929, Mar. 2008.

    6

  • VNE solution

    Two subproblems: Virtual Node Mapping (VNoM): maps virtual nodes to

    substrate nodes Virtual Link Mapping (VLiM): maps virtual links to

    substrate paths VNE algorithms address the VNoM while solving the VLiM

    using: the shortest-path (SP) algorithms or the Multicommodity Flow (MCF) algorithm

    October 6, 2015 IWCSN 2015, Perth, Australia 7

  • VNE solution: VLiM and path splitting

    The shortest-path algorithms do not permit path splitting: stricter than the MCF algorithm

    MCF enables path splitting: a flow may be divided into multiple flows with lower

    capacity flows are routed through various paths

    October 6, 2015 IWCSN 2015, Perth, Australia

    D. G. Andersen, Theoretical approaches to node assignment, Dec. 2002, Unpublished Manuscript. [Online]. Available: http://repository.cmu.edu/

    compsci/86/.

    8

  • VNE formulation: constrains

    Substrate network graph: Resources:

    substrate nodes: CPU capacity substrate links: bandwidth

    October 6, 2015 IWCSN 2015, Perth, Australia

    15

    15

    10

    10

    5 5

    10

    10

    5

    5

    5

    Gs(Ns, Es)

    C(ns)B(es)

    9

  • VNE formulation: constrains

    Virtual network graph: Resources:

    virtual nodes: CPU capacity virtual links: bandwidth

    October 6, 2015 IWCSN 2015, Perth, Australia

    10

    5 5

    15 5

    10

    G i(N i , E i)

    C(n i)B(e i)

    10

  • VNE objective

    Maximize the profit of InPs Contributing factors to the generated profit:

    embedding revenue embedding cost acceptance ratio

    October 6, 2015 IWCSN 2015, Perth, Australia

    M. Chowdhury, M. R. Rahman, and R. Boutaba, ViNEYard: Virtual network embedding algorithms with coordinated node and link mapping, IEEE/ACM Trans. Netw., vol. 20, no. 1, pp. 206219, Feb. 2012.

    L. Gong, Y. Wen, Z. Zhu, and T. Lee, Toward profit-seeking virtual network embedding algorithm via global resource capacity,

    in Proc. IEEE INFOCOM, Toronto, ON, Canada, Apr. 2014, pp. 19.

    11

  • VNE objective: revenue

    Maximize revenue:

    : weights for CPU requirements : weight for bandwidth requirements general assumption:

    October 6, 2015 IWCSN 2015, Perth, Australia

    R(G i) = wcX

    n i2N i

    C(n i) + wbX

    e i2E i

    B(e i)

    wb

    wc

    wc = wb = 1

    12

  • VNE objective: revenue

    Generated revenue is not a function of the embedding configuration: is constant regardless of the embedding

    configuration

    October 6, 2015 IWCSN 2015, Perth, Australia

    R(G i)

    13

  • VNE objective: cost

    Minimize the cost:

    : total allocated bandwidth of the substrate link for virtual link

    depends on the embedding configuration

    October 6, 2015 IWCSN 2015, Perth, Australia

    C(G i) =X

    n i2N i

    C(n i) +X

    e i2E i

    X

    es2Esfe i

    es

    fe i

    es

    e ies

    C(G i)

    14

  • VNE objective: acceptance ratio

    Maximize acceptance ratio:

    : number of accepted Virtual Network Requests (VNRs) in a given time interval

    : number of all arrived VNRs in

    October 6, 2015 IWCSN 2015, Perth, Australia

    pa =| a()|| ()|

    | a()|

    | ()|

    15

  • VNE objective function

    Objective of embedding a VNR is to maximize:

    : large negative penalty for unsuccessful embedding The upper bound:

    October 6, 2015 IWCSN 2015, Perth, Australia

    F( i) =

    R(G i)C(G i) successful embeddings otherwise

    F( i) 0

    16

  • VNE algorithms: R-Vine and D-Vine

    Formulate VNE problem as a Mixed Integer Program (MIP)

    Their objective is to minimize the cost of accommodating the VNRs

    Use a rounding-based approach to obtain a linear programming relaxation of the relevant MIP

    Use Multicommodity Flow algorithm for solving VLiM

    October 6, 2015 IWCSN 2015, Perth, Australia

    M. Chowdhury, M. R. Rahman, and R. Boutaba, ViNEYard: Virtual network embedding algorithms with coordinated node and link mapping, IEEE/ACM Trans. Netw., vol. 20, no. 1, pp. 206219, Feb. 2012.

    17

  • VNE algorithms: Global Resource Capacity (GRC) Node-ranking-based algorithm:

    computes a score/rank for substrate and virtual nodes employs a large-to-large and small-to-small mapping

    scheme to map the virtual nodes to substrate nodes Employs the Shortest-Path algorithm to solve VLiM Outperforms earlier similar proposals

    October 6, 2015 IWCSN 2015, Perth, Australia

    L. Gong, Y. Wen, Z. Zhu, and T. Lee, Toward profit-seeking virtual network embedding algorithm via global resource capacity,

    in Proc. IEEE INFOCOM, Toronto, ON, Canada, Apr. 2014, pp. 19.

    18

  • VNE algorithms: Global Resource Capacity (GRC) Calculates the embedding capacity for a substrate

    node :

    : damping factor : substrate link connecting and : normalized CPU resource of

    October 6, 2015 IWCSN 2015, Perth, Australia 19

    r(nsi )nsi

    r(nsi ) = (1 d)C(nsi ) + dX

    nsj2N (nsi )

    Bes(nsi , n

    sj)

    X

    nsk2N (nsj)

    Bes(nsj , n

    sk)

    es(nsi , nsj) n

    si n

    sj

    C(nsi ) nsi

    0 < d < 1

    C(nsi ) =C(nsi )P

    ns2Ns C(ns)

  • VNE algorithms: Global Resource Capacity (GRC) Matrix form:

    is a matrix:

    October 6, 2015 IWCSN 2015, Perth, Australia 20

    r = (1 d)c+ dMr

    c = (C(ns1), C(ns2), . . . , C(nsj))T

    r = (r(ns1), r(ns2), . . . , r(n

    sk))

    T

    M k-by-k

    mij =

    8>>>>>:

    Bes(nsi , n

    sj)

    X

    nsk2N (nsj)

    Bes(nsj , n

    sk) es(nsi , nsj) 2 Es

    0 otherwise

  • VNE algorithms: Global Resource Capacity (GRC) is calculated iteratively:

    Initially: Stop condition:

    October 6, 2015 IWCSN 2015, Perth, Australia 21

    rk+1 = (1 d)c+ dMrk

    r0 = c

    |rk+1 rk| < ,0 <

  • SDN and network virtualization

    Software-Defined Networking (SDN): separates network intelligence from network devices enables central implementation of network control

    logic SDN may enable cloud providers such as the Amazon

    Web Services to offer network virtualization services requires embedding of the virtual networks in data

    center networks

    October 6, 2015 IWCSN 2015, Perth, Australia 22

  • VNE in data center networks

    Data center networks have defined topologies: examples of two recent proposals:

    BCube and Fat-Tree Topological features significantly affect quality of the

    VNE solution Goal: identify the network topology that is better suited

    for VNE

    October 6, 2015 IWCSN 2015, Perth, Australia 23

  • Data center topologies: BCube

    Notation: : BCube level : number of hosts in the level-0 BCube

    Recursively structured Switches are not directly interconnected Hosts perform packet forwarding functions

    October 6, 2015 IWCSN 2015, Perth, Australia

    BCube(k, n)kn

    C. Guo, G. Lu, D. Li, H. Wu, X. Zhang, Y. Shi, C. Tian, Y. Zhang, and S. Lu, BCube: A

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