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Measurement based traffic engineering

Poul Heegaard,

Telenor R&D / NTNU Dept. Telematics

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Open architecture Performance guarantee Service differentiation

Measurements

Performance optimisationResource allocation

Test labs Production networks

Gilb’s Law:“Anything can be measured in a way that is superior to not measuring it at all.”

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Open architecturePerformance

guaranteeService

differentiations

Measurements

Test labs Production networks

Resource allocation Performance optimisation

Foster rich set applicationsWorldwide commercial

interests

New actors protect investmentsNew cost modelsQoS guarantees

New QoS requirements

New applications, New markets

New QoS requirements

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Performance optimisation

Resource control

Open architecture Performance guarantee Service differentiations

Measurements

Test labsProduction networks

(core/AS, access/SPE/CPE)

Controllable (Protect, Priority, Guarantee)

Proactive (planning, designing)Reactive (monitoring, reconfig)

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Measurements- domain (inter, intra, access, private)- level (physical, network, transport, application)- approach (active, passive)

Open architecture(service innovation, fairness)

Performance guarantee Service differentiation

Performance optimisationResource allocation

Test labs Production network

– SLA fulfilled?– Mechanisms effective? – Effect of new applications?– New applications appeared?– Performance bottlenecks?– Connectivity? – Routing stability?– “Clever” users?– “Malicious” users?– Charging?

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Open architecture Performance guarantee Service differentiations

Measurements

Performance optimisationResource allocation

Test labs

Production networks- redesign, configuration- connectivity- performance assurance- traffic trends - security - input to traffic modelling

Essential: multipurpose measurement architecture

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Multiple measurement objectives

Measurement Application

IP Network QoS & Performance managementMEASUREMENT FUNCTIONS

PrognosisTrends

Traffic matrix

SecurityFraud

DependabilityReliability

Overload(observation and control)

Accounting SLA validation

Planning / Long term operation Medium / Short term operation

Multipurpose measurement probe

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Many options

ADSL 1-

Peering network Our own network

Service provider

POP

DoS attack?traffic volume?

DoS attack?Traffic volume (per customer)?service usage?

End-to-end delay,delay variation (“jitter”), packet loss ratio

Asymmetric traffic?DoS attack?traffic trends?resource (e.g. link) utilisation?

Volume per customer?DoS attack?

Paradigm shift in networking: same platform for all servicesShould also apply to monitoring: same platform for all measurement needs

less than 5% overhead

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Open architecture Performance guarantee Service differentiations

Measurements

Performance optimisationResource allocation

Test labs

Production networks- coarse grained data collection- imprecise active tests- performance demanding- excessive measurement data- increasing measurement needs- measurements by 3rd party - measurement architecture hard

Essential: configurable, precise, up-to-date, available data

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Ex: Delay estimation

• two-way by single point (e.g. in tcp, rtp flows)• one-way by dual point (e.g. inexpensive probes)

upper boundson networklayer delay

protocol effects(e.g. delayed ack)

congestion

no congestion=> perfect match

compare single and dual point estimations

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Ex: flow-sampling

• Data reduction by sampling

0.2 0.4 0.6 0.8 1

20

40

60

80

100 Quantileplot, mpeg traffic

Full trace and 0.099% poisson sampling

Sampled trace

Delay (ms)

Full trace

Quantiles

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Ex: corrected active tests

customer view

provider view

Measure quality any time

Measure quality when service is in use

Validation of delay measured by active tests

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100

200

300

400

500

600

700

800

900

1000

0,5 0,6 0,7 0,8 0,9 1 1,1 1,2 1,3

[ms]

Load level for offered tcp traffic

corrected packet delay

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Test labs

Open architecture Performance guarantee Service differentiations

Measurements

Performance optimisationResource allocation

Production networks

- equipment- mechanisms (e.g. QoS)- configurations- applications- user behaviour

Essential: realistic traffic generator => e.g. GenSyn

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GenSyn - objectives

New network mechanisms

•Controllable•Scalable•Re-producible•Realistic traffic

User behaviour model

Internet protocols

User behaviour model

Internet protocols

New services

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GenSyn – in short

• Java-based, portable traffic generator• Flexible and scalable• Stochastic state models of user behaviour• Link to protocol stack for real packet generation

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Conflicting and interdependent interests

Gordian Knot

simplisity

openess

resource control

QoS guaranteedifferentiation

security

monitoring

segmentation

protection

revenue

real-time

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The solution?