Post on 20-Feb-2017
Soka University
Controller placement algorithm to alleviate burdens on communication nodes
Genya IshigakiNorihiko Shinomiya Graduate School of Engineering,Soka University, Japan
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Introduction
2
Software Defined Networking• decoupling of routing logic from forwarding functions • partitioning of the data plane into some clusters [Aoki et al., 2015] • involving a distributed controller in each cluster
Controller Placement Problem for in-band SDN• in-band: attaching a controller to a switch in a data plane • optimizing some evaluation function
ControlPlane
Southbound Channel
Data Plane
(a) Out-of-Band Model (b) In-Band Model
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Introduction: Related Placement Problems
3
Evaluation Criteria
distance-based latency
survivability
burdens on controllers
inter-clusters
intra-cluster
Range for Controller Assignment
metrics btwn a controller and switches in a cluster
metrics btwn controllers [Hock et al., 2013]
the number of edge disjoint paths [Hu et al., 2013]
the number (weights) of switches under a controller [Yao et al., 2014]
the furthest distance to a switch from a controller [Heller et al., 2012]
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Focus of this Study
4
• defining a burden of each switch based on the stress centrality • proposing a controller placement to alleviate the burden
Categorization• different objective metric (burdens on switches) • intra-cluster placement
burdens on switches
distance-based latency
survivability
burdens on controllers
inter-clusters
intra-cluster
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controller v1
v2
v3
v4
C
controller
?
e1
e2e3
e4
e5
Model and Attributes
5
General Controller Placement ProblemInput: a set of switches that can be assigned a controller Output: one of the switches (the place for the controller) Task: minimization of some function on the placement
Graph modeling Network switches: vertices
cables: edges
V = {vi}
E = {ek}
vj 2 VC ✓ V
Minimize f(vj)
subject to vj 2 C
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Problem Formulation
6
Traversal Set for a Vertexa set of shortest paths containing for any
MinimizeX
vi2V \{vj}
bj(vi)
* is a switch assigned the controller.vj
bj(vi) = |Tj(vi)|
Tj(vi)Tj(vi) = {Pnj} vn 2 Vvi
Burden on a Nodethe size of the traversal set
Objective Function (Alleviation of Node Burdens)
cntlr The size of a traversal set of this node is 3 (including itself).
The size of a traversal set of this node is 1.
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Placement alleviating the burden of a node
7
Complexity• When ,
• When ,
|C| ⌧ |V |
|C| ⇡ |V |
O(|C|(|E|+ |V | log |V |))
O(|V |3)
FOR each controller candidate Obtaining a set of all shortest paths to from :
FOR each FOR each vertex
counting the number of paths using ( )
Pi
vjbi(vj)
vj 2 Vvivi 2 C ✓ V
Pi
vj
Procedure to decide the placement
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Simulation: Evaluation Criteria
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1. Burdens on nodes: the objective function of our study
2. Latency: the longest distance between switches and their controller
3. Survivability: the number of edge disjoint paths between switches and a controller
4. Worst Path Availability: the worst probabilistic reliability of a path
L(vj) = max
vi2V \vjdist(vi, vj)
* is a switch assigned the controller.vj
S(vj) =
Pvi2V \{vj} �ij
|V |� 1
B(vj) =X
vi2V \{vj}
bj(vi) =X
vi2V \{vj}
|Tj(vi)|
W (vj) = minvi2V \{vj}
A(Pij) = minvi2V \{vj}
Y
e2Pij
A(e)
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Simulation: Comparison with Other Placements
9
1. closeness center placement [Heller et al., 2012]minimizing the longest distance from the controller to any other node
2. the best value of the other placements
3. the average of the other placements
4. the optimum value (OPT)
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Simulation: Settings
10
Graphs (with 20, 40, 60, 80, 100 vertices)• Barabási Albert (BA) random graph • Newman Watts Strogatz (NWS) random graph • GNP random graph • GEANT2012 (40 nodes, 61 edges, the max degree: 10)
[The Internet Topology Zoo, 2015]
NLBE
DK
DE
LT
UKIE
RU
IS
NO
SE
EE
CZ
LU
CH
CY
IL
AT
LV
FI
PL
UA
BY
SK
FR
ESIT
GR
MT
MD
RO
TR
HU
BG
MK
HR
RS
ME
SLPT
0
4
5 8
6
7
9
1
2
3
NWS GEANT2012
Random Edge Weights• distance • availability : probabilistic reliability of an edge
dist(vi, vj) 2 R+
A(ek) 2 [0.0, 1.0)
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20
25
30
35
40
45
50
55
60
65
20 40 60 80 100
Late
ncy
(dis
tanc
e-ba
sed)
Number of Nodes
proposed placementcloseness center placement
average of other placements
0
100
200
300
400
500
600
700
800
900
20 40 60 80 100
Tota
l Bur
den
on N
odes
Number of Nodes
proposed placementcloseness center placement
best of other placementsaverage of other placements
Simulation Results: NWS Random Graph I
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1. Burdens on nodes • optimum • increasing gap with
closeness center placement (along with the size of a graph)
2. Latency• Closeness center
placement: optimum • better than the
average of other placements
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1.8
1.9
2
2.1
2.2
2.3
2.4
2.5
2.6
20 40 60 80 100
Num
ber o
f Disj
oint
Pat
hs (A
vera
ge)
Number of Nodes
proposed placementcloseness center placement
average of other placementsOPT
0
0.005
0.01
0.015
0.02
0.025
20 40 60 80 100
Wor
st P
ath
Avai
labi
lity
Number of Nodes
proposed placementcloseness center placement
OPT
Simulation Results: NWS Random Graph II
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3. Survivability• close to the optimum • the reduction of burdens
causing the increase of disjoint paths for scattering msgs
4. Worst Path Availability• long paths possibly
containing many nodes • tendency to decrease the
diameter of the graph • increase in the product of
availabilitiesY
e in P
A(e) (0.0 < A(e) 1.0)
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Conclusion
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• in-band Software Defined Networking• Purpose of this Study
a controller placement algorithm to reduce the burdens on switches in a data plane
• Modeling each network portion governed by a controller as a graph
• the burden on a nodethe number of shortest paths between each node and their controller
• Simulation• the total burden on all nodes • latency • survivability • worst path availability
• Future Workimplementation using the OpenDaylight Framework