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.
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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
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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/.
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VNE formulation: constrains
Substrate network graph: Resources:
substrate nodes: CPU capacity substrate links: bandwidth
October 6, 2015 IWCSN 2015, Perth, Australia
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15
10
10
5 5
10
10
5
5
5
Gs(Ns, Es)
C(ns)B(es)
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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)
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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.
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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
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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)
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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)
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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()|
| ()|
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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
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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.
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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.
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VNE algorithms: Global Resource Capacity (GRC) Calculates the embedding capacity for a substrate
node :
: damping factor : substrate link connecting and : normalized CPU resource of
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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:
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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:
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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
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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
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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
