Soka University

Controller placement algorithm to alleviate burdens on communication nodes

Genya IshigakiNorihiko Shinomiya Graduate School of Engineering,Soka University, Japan

Soka University

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

8

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

11

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

12

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

13

• 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