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Measurement-aware Monitor Placement andRouting

A Joint Optimization Approach for Network-WideMeasurements

Guanyao Huang1 Chia-Wei Chang2 Chen-Nee Chuah1

Bill Lin2

1University of California at Davis, CA USA2

University of California at San Diego, CA USA

2010

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task: measure network traffic with

limited resources and QoS constraintsin mind

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question: which monitors should beactivated, how to route traffic over

them to maximize measurement gain,given limited resources

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current state of the art

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pick monitor locations without

changing routing decison

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might miss important traffic

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decide on routing based on static

monitors (MeasuRouting)

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might violate QoS constraints

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MMPR Motivational Example

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find monitor locations first, then use

MeasuRouting

i i l i f i

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maximize measurement resolution function

=

y

(i,j)

E

Iyp(i,j)u(i,j)xyx

G(V,E) - network graphV - set of nodesE - set of edges

(i, j

) - tuple denoting edge inE

- mutually exclusive flowsetsx - an OD pairx - set of flowsets belonging to OD pair xy - flowset, part of an x

Iy - measurement utility of flowset yu(i,j) - boolean monitor placement for link (i, j)pi,j - sampling rate of link (i, j)xyx - original routing

y

(i,j)

E

- traffic demand flowset y places on link (i, j)y - traffic demand of flowset y

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search for best y(i,j)

and u(i,j) assignments in network with Mnodes, minimizing with limiting amount of monitors to K

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K-Best algorithm

1. start with All-On configuration, calculate maximum andoptimal traffic assignment yi,j

2. rank monitors according to a metric

least utility

y p(i,j)

y

(i,j)I

y least traffic

y

y

(i,j)y least importance

y

y

(i,j)Iy

least rate p(i,j) least neighbours

3. remove the top M-K monitors

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Successive Selection

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Greedy Algorithm

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Quasi-Greedy Algorithm

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Experimental Evaluation

Iy =

fvy ifbf Abilene

public academic network in the US 11 nodes 28 10Gbps links

AS6461 RocketFuel (Topology Mapping Engine) topology 19 nodes 68 links

GEANT European research/education network 23 nodes 74 (155Mbps - 10Gbps) links

O O

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All-On, Placement-only, MR-only, Optimal

MMPR f i K B

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MMPR performance using K-Best

MMPR f i S i S l i

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MMPR performance using Successive Selection

MMPR f i Q i G d

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MMPR performance using Quasi-Greedy

C diff t h i ti

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Compare different heuristics

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best choice: K-Best using least utility

reduces computation time by 23X, 246X and 233X forAbilene, AS6461 and GEANT respectively

produces near optimal solution

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opportunities sampling rates as another degree of freedom future implementation in OpenFlow (programmable routing

platform)

issues in practice how to select traffic importance? what routing protocol? how to estimate flow importance dynamically? how to configure routing tables dynamically?

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questions?