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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